Transferring a partial task in a distributed computing system

ABSTRACT

A method begins by a dispersed storage (DS) processing module determining that partial task processing resources of a first DST execution unit are projected to be available. The method continues with the DS processing module ascertaining that partial task processing resources of a second DST execution unit are projected to be overburdened. The method continues with the DS processing module receiving, from the second DST execution unit, a partial task assigned to the second DST execution unit in accordance with a partial task allocation transfer policy to produce an allocated partial task and executing the allocated partial task.

CROSS REFERENCE TO RELATED PATENTS

This patent application is claiming priority under 35 USC §119 to aprovisionally filed patent application entitled REDISTRIBUTING DATA IN ADISTRIBUTED STORAGE AND TASK NETWORK having a provisional filing date ofJan. 31, 2012, and a provisional Ser. No. 61/593,126.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION

1. Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

2. Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work station, video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40 is a flowchart illustrating an example of redistributing dataand tasks in accordance with the present invention;

FIG. 41A is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 41B is a flowchart illustrating an example of transferring apartial task in accordance with the present invention;

FIG. 41C is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 41D is a flowchart illustrating another example of transferring apartial task in accordance with the present invention;

FIG. 42 is a flowchart illustrating another example of acquiring a taskin accordance with the present invention;

FIG. 43 is a flowchart illustrating an example of balancing tasks inaccordance with the present invention;

FIG. 44 is a flowchart illustrating another example of balancing tasksin accordance with the present invention;

FIG. 45 is a flowchart illustrating another example of balancing tasksin accordance with the present invention;

FIG. 46A is a flowchart illustrating an example of determining a slicegrouping in accordance with the present invention;

FIG. 46B is a diagram illustrating an example of a dispersed storage andtask execution unit to pillar mapping in accordance with the presentinvention;

FIG. 47A is a schematic block diagram of another example of a dispersedstorage and task execution unit in accordance with the presentinvention;

FIG. 47B is a schematic block diagram of an example of a dispersedstorage network in accordance with the present invention;

FIG. 47C is a schematic block diagram of another example of a dispersedstorage network in accordance with the present invention;

FIG. 47D is a flowchart illustrating an example of securely and reliablystoring data in accordance with the present invention;

FIG. 47E is a schematic block diagram of another example of a dispersedstorage network in accordance with the present invention;

FIG. 47F is a flowchart illustrating another example of securely andreliably storing data in accordance with the present invention;

FIG. 48A is a schematic block diagram of another example of a dispersedstorage network in accordance with the present invention;

FIG. 48B is a schematic block diagram of another example of a dispersedstorage network in accordance with the present invention;

FIG. 48C is a flowchart illustrating an example of improving storageefficiency in accordance with the present invention;

FIG. 48D is a schematic block diagram of another example of a dispersedstorage network in accordance with the present invention;

FIG. 48E is a flowchart illustrating another example of improvingstorage efficiency in accordance with the present invention;

FIG. 49 is a flowchart illustrating an example of encrypting data inaccordance with the present invention; and

FIG. 50 is a flowchart illustrating an example of decrypting data inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes user devices 12 and/or user devices14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interfaces 30support a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g. or dispersed storage error codingparameters) include data segmenting information (e.g., how many segmentsdata (e.g., a file, a group of files, a data block, etc.) is dividedinto), segment security information (e.g., per segment encryption,compression, integrity checksum, etc.), error coding information (e.g.,pillar width, decode threshold, read threshold, write threshold, etc.),slicing information (e.g., the number of encoded data slices that willbe created for each data segment); and slice security information (e.g.,per encoded data slice encryption, compression, integrity checksum,etc.).

The DSTN managing module 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsincludes, but is not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39. With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terra-Bytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terra-Bytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 90 and the task(s) 94. The outbound DST processingsection 80 processes the data 90 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 90 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit 36 performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units 36 send, via the network 24, their partial results102 to the inbound DST processing section 82 of the DST client module34. The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terra-Bytes) into 100,000 data segments, each being 1 Giga-Byte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The group selecting module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the group selecting modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The group selecting module 114 outputs the slice groupings96 to the corresponding DST execution units 36 via the network 24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, wherein in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if, the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or and the other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to the selected DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data in based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy checkCRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Salomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of datasegments 156 for a given data partition, the slicing module outputs aplurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45) and receives segmenting information (i.e.,control information 160) from a control module. Each data block may beof the same size as other data blocks or of a different size. Inaddition, the size of each data block may be a few bytes to megabytes ofdata. As previously mentioned, the segmenting information indicates howmany rows to segment the data partition into, indicates how many columnsto segment the data partition into, and indicates how many columns toinclude in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first-third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slices of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices include error correctiondata based on the first-third words of the second data segment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. In this example, a grouping selection module 114organizes the encoded data slices into five slice groupings (e.g., onefor each DST execution unit of a distributed storage and task network(DSTN) module). As a specific example, the grouping selection module 114creates a first slice grouping for a DST execution unit #1, whichincludes first encoded slices of each of the sets of encoded slices. Assuch, the first DST execution unit receives encoded data slicescorresponding to data blocks 1-15 (e.g., encoded data slices ofcontiguous data).

The grouping selection module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selection module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selection module 114 creates a fourth slice grouping forDST execution unit #4, which includes fourth encoded slices of each ofthe sets of encoded slices. As such, the fourth DST execution unitreceives encoded data slices corresponding to first error encodinginformation (e.g., encoded data slices of error coding (EC) data). Thegrouping selection module 114 further creates a fifth slice grouping forDST execution unit #5, which includes fifth encoded slices of each ofthe sets of encoded slices. As such, the fifth DST execution unitreceives encoded data slices corresponding to second error encodinginformation.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may varying in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or andthe other analog and/or digital processing circuitry), availability ofthe processing resources, etc. If the controller 86 determines that theDT execution module(s) 90 have sufficient capabilities, it generatestask control information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results may 102 also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identity of other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 andthe memory 88. For example, when the partial task 98 includes aretrieval request, the controller 86 outputs the memory control 174 tothe memory 88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step executing the task in accordance with the task controlinformation 176, the DT execution module 90 retrieves the encoded slicesfrom memory 88. The DT execution module 90 then reconstructs contiguousdata blocks of a data partition. As shown for this example,reconstructed contiguous data blocks of data partition 1 include datablocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST executions units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of their corresponding partial tasks 102 on the correspondingslice groupings to produce partial results 102. The inbounded DSTprocessing section 82 receives the partial results 102 via thedistributed task control module 188. The inbound DST processing section82 then processes the partial results 102 to produce a final result, orresults 104. For example, if the task was to find a specific word orphrase within data, the partial results 102 indicate where in each ofthe prescribed portions of the data the corresponding DST executionunits found the specific word or phrase. The distributed task controlmodule 188 combines the individual partial results 102 for thecorresponding portions of the data into a final result 104 for the dataas a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieve slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180; provides the DSerror encoding parameters to the DS error decoding module 182; andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, the results of processinginformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result, or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings using a de-groupingselector 180 control by a control signal 190 as shown in the example toproduce a plurality of sets of encoded data slices 122. Each setcorresponding to a data segment of the data partition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC)verification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Salomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 sliceand provides encoded data 156. In this example, a decode threshold isthree. As such, each set of encoded data slices 158 is shown to havethree encoded data slices per data segment. The de-slicing module 204may receive three encoded data slices per data segment because anassociated distributed storage and task (DST) client module requestedretrieving only three encoded data slices per segment or selected threeof the retrieved encoded data slices per data segment. As shown, whichis based on the unity matrix encoding previously discussed withreference to FIG. 8, an encoded data slice may be a data-based encodeddata slice (e.g., DS1_d1&d2) or an error code based encoded data slice(e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce data segments 154. In this example,data segment 1 includes 3 rows with each row being treated as one wordfor encoding. As such, data segment 1 includes three words: word 1including data blocks d1 and d2, word 2 including data blocks d16 andd17, and word 3 including data blocks d31 and d32. Each of data segments2-7 includes three words where each word includes two data blocks. Datasegment 8 includes three words where each word includes a single datablock (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of a de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desired to distributedly store in the DSTN module. The data 92 may bea file (e.g., video, audio, text, graphics, etc.), a data object, a datablock, an update to a file, an update to a data block, etc. In thisinstance, the outbound DST processing module 80 converts the data 92into encoded data slices 216 as will be further described with referenceto FIGS. 21-23. The outbound DST processing module 80 sends, via thenetwork 24, to the DST execution units for storage as further describedwith reference to FIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution units retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupselection module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC),etc.). The control information 160 indicates which steps of the DS errorencoding are active for the data 92 and, for active steps, indicates theparameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The group selecting module 114 groups the encoded slices 218 of the datasegments into pillars of slices 216. The number of pillars correspondsto the pillar width of the DS error encoding parameters. In thisexample, the distributed task control module 118 facilitates the storagerequest.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Salomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selection module organizes the sets of encodeddata slices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selection module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selection module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186. The dispersed error decoding module 182 is operableto de-slice and decode encoded slices per data segment 218 utilizing ade-slicing and decoding function 228 to produce a plurality of datasegments that are de-segmented utilizing a de-segment function 230 torecover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC) verification, etc.), and/or any other typeof digital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from the controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Salomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terra-Bytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distributions modules location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task

sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few terra-bytes or more),addressing information of Addr_(—)1_AA, and DS parameters of ⅗;SEG_(—)1; and SLC_(—)1. In this example, the addressing information maybe a virtual address corresponding to the virtual address of the firststorage word (e.g., one or more bytes) of the data and information onhow to calculate the other addresses, may be a range of virtualaddresses for the storage words of the data, physical addresses of thefirst storage word or the storage words of the data, may be a list ofslices names of the encoded data slices of the data, etc. The DSparameters may include identity of an error encoding scheme, decodethreshold/pillar width (e.g., ⅗ for the first data entry), segmentsecurity information (e.g., SEG_(—)1), per slice security information(e.g., SLC_(—)1), and/or any other information regarding how the datawas encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_(—)2_XY, andDS parameters of ⅗; SEG_(—)2; and SLC_(—)2. In this example, theaddressing information may be a virtual address corresponding to thevirtual address of the first storage word (e.g., one or more bytes) ofthe task and information on how to calculate the other addresses, may bea range of virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., ⅗ for the first data entry),segment security information (e.g., SEG_(—)2), per slice securityinformation (e.g., SLC_(—)2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task

sub-task mapping information table 246 includes a task field 256 and asub-task field 258. The task field 256 identifies a task stored in thememory of a distributed storage and task network (DSTN) module and thecorresponding sub-task fields 258 indicates whether the task includessub-tasks and, if so, how many and if any of the sub-tasks are ordered.In this example, the task

sub-task mapping information table 246 includes an entry for each taskstored in memory of the DSTN module (e.g., task 1 through task k). Inparticular, this example indicates that task 1 includes 7 sub-tasks;task 2 does not include sub-tasks, and task k includes r number ofsub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or and other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words or/or phrases intranslated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases. The translated data 282 is translatedback 308 (e.g., sub-task 1_4) into the language of the original data toproduce re-translated data 284. These two tasks are dependent on thetranslate task (e.g., task 1_3) and thus must be ordered after thetranslation task, which may be in a pipelined ordering or a serialordering. The re-translated data 284 is then compared 310 with theoriginal data 92 to find words and/or phrases that did not translate(one way and/or the other) properly to produce a list of incorrectlytranslated words 294. As such, the comparing task (e.g., sub-task 1_5)310 is ordered after the translation 306 and re-translation tasks 308(e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of ⅗ for their decode threshold/pillar width; hence spanningthe memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done the by DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2 _(—) z) and addressing informationfor each partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2 _(—) z by DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1 search for non-words in data partitions 2_1 through 2 _(—)z to produce task 1_1 intermediate results (R1-1, which is a list ofnon-words). Task 1_2 (e.g., identify unique words) has similar taskexecution information as task 1_1 to produce task 1_2 intermediateresults (R1-2, which is the list of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2_5 through 2 _(—) z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3 _(—) z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2 _(—) z) with partitions of task 1-4 intermediate resultspartitions R1-4_1 through R1-4_z to produce task 1_5 intermediateresults (R1-5, which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1 _(—) z) with partitions oftask 1-5 intermediate results partitions (R1-5_1 through R1-5 _(—) z) toproduce task 1_6 intermediate results (R1-6, which is the listtranslation errors due to non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2 _(—) z) with partitions oftask 1-5 intermediate results partitions (R1-5_1 through R1-5 _(—) z) toproduce task 1_7 intermediate results (R1-7, which is the list ofcorrectly translated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2 _(—) z by DT executionmodules 3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases indata partitions 2_1 through 2 _(—) z to produce task 2 intermediateresults (R2, which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3 _(—) z by DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3 _(—) z) toproduce task 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32) executes task 1_1 to produce a secondpartial result 102 of non-words found in the second data partition. Thesets of DT execution modules (as per the DST allocation information)perform task 1_1 on the data partitions until the “z” set of DTexecution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1through R1-1 _(—) m). If the first intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_2 to produce the second intermediate result (R1-2), whichis a list of unique words found in the data 92. The processing module ofDST execution 1 is engaged to aggregate the first through “zth” partialresults of unique words to produce the second intermediate result. Theprocessing module stores the second intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1through R1-2 _(—) m). If the second intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2 _(—) z). For the datapartitions, the allocated set of DT execution modules 90 executes task1_3 to produce partial results 102 (e.g., 1^(st) through “zth”) oftranslated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_3 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1through R1-3 _(—) y). For each partition of the third intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes ⅗ decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 2-6 per the DST allocationinformation).

As is further shown in FIG. 35, the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3 _(—) z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_1 through R1-4 _(—) z). For each partition of the fourthintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 3-7 per the DSTallocation information).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35. To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 1_5 to produce the fifth intermediate result (R1-5), which is thelist of incorrectly translated words and/or phrases. In particular, theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of the list of incorrectly translatedwords and/or phrases to produce the fifth intermediate result. Theprocessing module stores the fifth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5 _(—)z). For each partition of the fifth intermediate result, the DST clientmodule uses the DS error encoding parameters of the data (e.g., DSparameters of data 2, which includes ⅗ decode threshold/pillar widthratio) to produce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_6 to produce the sixth intermediate result (R1-6), which is thelist of incorrectly translated words and/or phrases due to non-words. Inparticular, the processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases due to non-words to producethe sixth intermediate result. The processing module stores the sixthintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6 _(—)z). For each partition of the sixth intermediate result, the DST clientmodule uses the DS error encoding parameters of the data (e.g., DSparameters of data 2, which includes ⅗ decode threshold/pillar widthratio) to produce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_7 to produce the seventh intermediate result (R1-7), which is thelist of correctly translated words and/or phrases. In particular, theprocessing module of DST execution 3 is engaged to aggregate the firstthrough “zth” partial results of the list of correctly translated wordsand/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes ⅗ decode threshold/pillar widthratio) to produce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terra-Byte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40 is a flowchart illustrating an example of redistributing dataand tasks. The method begins with step 350 where a processing module(e.g., of a distributed storage and task (DST) client module) detects aDST execution unit with an unfavorable partial task execution level. Thedetecting includes at least one of initiating a query, receiving a taskresponse, receiving a message, obtaining a partial task execution level,and comparing the partial task execution level to an execution levelthreshold. The execution level threshold includes a too fast thresholdand a too slow threshold. For example, the processing module detects theDST execution unit with the unfavorable partial task execution levelwhen the partial task execution level is less than the too slowthreshold. As another example, the processing module detects the DSTexecution unit with the unfavorable partial task execution level whenthe partial task execution level is greater than the too fast threshold.

The method continues at step 352 where the processing module identifiesone or more other DST execution units with a complementary partial taskexecution level. A complementary partial task execution level includesan execution level that is too fast when the DST execution unit is tooslow and an execution level that is too slow when the DST execution unitis too fast. The identifying includes at least one of initiating aquery, receiving a task response, receiving a message, obtaining apartial task execution level corresponding to another DST executionunit, and comparing the partial task execution level corresponding tothe other DST execution to the partial task execution level.

The method continues at step 354 where the processing module selects oneor more slices of one or more slice groups stored at a source DSTexecution unit associated with a partial task execution level that isbelow a low threshold. For example, the processing module selects slicesof the DST execution unit when the DST execution unit is too slow (e.g.,the partial task execution level of the DST execution unit is less thanthe too slow threshold). As another example, the processing moduleselects slices of another DST execution unit when the DST execution unitis too fast (e.g., the partial task execution level of the DST executionunit is greater than the too fast threshold).

The method continues at step 356 where the processing module selects adestination DST execution unit associated with the partial taskexecution level above a high threshold. For example, the processingmodule selects the DST execution unit as the destination DST executionunit when the DST execution unit is too fast. As another example, theprocessing module selects the other DST execution unit as thedestination DST execution unit when the DST execution unit is too slow.

The method continues at step 358 where the processing module identifiespartial tasks associated with the one or more slices of one or moreslice groups. The identifying includes retrieving DST allocationinformation from at least one of a source DST execution unit and a DSTclient module associated with partial task execution.

The method continues at step 360 where the processing module facilitatestransferring the partial tasks and the one or more slices of the one ormore slice groups from the source DST execution unit to the destinationDST execution unit. The facilitating includes selecting the slices,identifying associated partial tasks, transferring the slices, andtransferring the partial tasks. The selecting the slices includesselecting a number of slices to be transferred of the one more slices ofthe one or more slice groups based on a difference between the partialtask execution level of the DST execution unit and the partial taskexecution level of the other DST execution unit such that an expectedpartial task execution level of the DST execution unit and an expectedpartial task execution level of the other DST execution unit arefavorable (e.g., after transferring the partial tasks).

The transferring the slices includes at least one of sending a slicetransfer request to the source DST execution unit with regards to slicesto be transferred, retrieving the slices to be transferred from thesource DST execution unit, and sending the slices to be transferred tothe destination DST execution unit. The identifying the associatedpartial tasks includes at least one of accessing DST allocationinformation with regards to the slices to be transferred to identify theassociated partial tasks, a look up, and receiving the partial tasks.The transferring the partial tasks includes at least one of sending apartial task transfer request to the source DST execution unit withregards to partial tasks associated with the slices to be transferred,retrieving the partial tasks from the source DST execution unit,retrieving the partial tasks from a DST client module, and sending thepartial tasks to the destination DST execution unit.

The method continues at step 362 where the processing module updates aslice location table to indicate that transfer slices are now associatedwith the destination DST execution unit and are disassociated with thesource DST execution unit. For example, the processing module modifiesDST allocation information accordingly. In addition, the processingmodule may facilitate updating one more slice groupings of associatedencoded data slices (e.g., that include error recovery information,i.e., error coded slices) when the transferring of the partial tasks andthe one more slices of the one or more slice groups is complete.

FIG. 41A is a schematic block diagram of another embodiment of adistributed computing system that includes a distributed storage andtask (DST) execution unit set 370. The DST execution unit set 370includes a set of DST execution units 372, 374, through 376. A first DSTexecution unit 372 includes a computing device 378. The computing device378 may be implemented utilizing at least one of a server, a storageunit, a storage server, a storage module, a dispersed storage (DS)processing unit, a DS unit, a DST execution unit, a user device, a DSTprocessing unit, and a DST processing module. The computing device 378includes a dispersed storage (DS) module 380. The DS module 380 may beimplemented utilizing at least one of a processing module, one or morecentral processing unit cores, a software algorithm, a DST clientmodule, and a DS processing module. The DS module 380 includes anidentify underutilized resource module 382, an identify overburdenedresource module 384, a transfer task module 386, and an execute taskmodule 388.

The set of DST execution units 370 is assigned to perform tasks on largeamounts of data. Each of the large amounts of data is partitioned intodata partitions and each of the data partitions is further divided intodata groups. Each of the tasks are divided into a set of partial tasksand various DST execution units of the set of DST execution units 370are assigned various partial tasks of various ones of the sets ofpartial tasks to perform on various ones of the data groups of variousones of the data partitions of various ones of the large amounts ofdata. In such a system, a high degree of variance between expected taskexecution and actual task execution may occur when an abundance ofvariability exists with regards to at least one of the initial taskassignment (e.g., randomness from externally assigned tasks) and taskscheduling (e.g., having a central function). The system functionsinclude identifying an underutilized resource, identifying anoverburdened resource, facilitating transfer of a task from theoverburdened resource to the underutilized resource, and facilitatingexecution of the task by the underutilized resource to provide a taskexecution efficiency improvement for the system.

With regards to identifying an underutilized resource, the identifyunderutilized resource module 382 determines that partial taskprocessing resources of the first DST execution unit 372 are projectedto be available based on a first local task queue, a first expectedpartial task performance factor, and a first expected partial taskallocation factor. The identify underutilized resource module 382determines that the partial task processing resources of the first DSTexecution unit are projected to be available by a series of determiningsteps. A first determining step includes the identify underutilizedresource module 382 determining that a current snapshot of the firstlocal task queue compares favorably to a current queue threshold. Asecond determining step includes the identify underutilized resourcemodule 382 determining that a projected snapshot of the first local taskqueue compares favorably to a projected queue threshold that is based onat least one of the first expected partial task performance factor andthe first expected partial task allocation factor. When the currentsnapshot of the first local task queue compares favorably to the currentqueue threshold and the projected snapshot of the first local task queuecompares favorably to the projected queue threshold, a third determiningstep includes the identify underutilized resource module 382 indicatingthat the partial task processing resources of the first DST executionunit are projected to be available producing an availability indication390.

With regards to identifying an overburdened resource, the identifyoverburdened resource module 384 ascertains that partial task processingresources of a second DST execution unit 374 of the set of DST executionunits 370 are projected to be overburdened based on a second local taskqueue, a second expected partial task performance factor (e.g., when theunit will get to the task, how long will it take to perform the task,proficiency at performing the task, etc.), and a second expected partialtask allocation factor (e.g., how much and what type of tasks). Theidentify overburdened resource module 384 identifies (e.g., via a query,a lookup) the second DST execution unit 374 based on a common taskcriteria with the first DST execution unit 372, wherein the common taskcriteria includes one or more of a common site, a common large amount ofdata, a common task allocation unit, and a common data partition. Theidentify overburdened resource module 384 ascertains by one or more ofreceiving an ascertaining indication (e.g., from the second DSTexecution unit 374) and determining based on obtaining one or more ofthe second local task queue, the second expected partial taskperformance factor, and the second expected partial task allocationfactor (e.g., from the second DST execution unit 374). The identifyoverburdened resource module 384 issues a performance request 394 andreceives a performance response 396 that includes one or more of theascertaining indication, the second local task queue, the secondexpected partial task performance factor, and the second expectedpartial task allocation factor.

The identify overburdened resource module 384 ascertains that thepartial task processing resources of the second DST execution unit 374of the set of DST execution units 370 are projected to be overburdenedby a series of ascertaining steps. A first ascertaining step includesthe identify overburdened resource module 384 ascertaining (e.g.,receive via performance response 396, initiating a test, determining)that a current snapshot of the second local task queue comparesunfavorably to a current queue threshold. A second ascertaining stepincludes the identify overburdened resource module 384 ascertaining(e.g., receive via performance response 396, initiating a test,determining) that a projected snapshot of the second local task queuecompares unfavorably to a projected queue threshold that is based on atleast one of the second expected partial task performance factor and thesecond expected partial task allocation factor. When at least one of thecurrent snapshot of the second local task queue compares unfavorably tothe current queue threshold and the projected snapshot of the secondlocal task queue compares unfavorably to the projected queue threshold,a third ascertaining step includes the identify overburdened resourcemodule 384 indicating that the partial task processing resources of thesecond DST execution unit 374 are projected to be overburdened byproducing an overburdened indication 392.

With regards to facilitating transfer of the task from the overburdenedresource to the underutilized resource, the transfer task module 386receives, from the second DST execution unit 374, a partial task 398assigned to the second DST execution unit 374 in accordance with apartial task allocation transfer policy to produce an allocated partialtask 400. The transfer task module 386 receives the partial task by aseries of receiving steps. A first receiving step includes the transfertask module 386 determining unexecuted partial tasks assigned to thesecond DST execution unit 374. The determining includes issuing a taskinformation request 402 to the second DST execution unit 374 andreceiving a task information response 404 that includes a list ofunexecuted partial tasks assigned to the second DST execution unit 374.A second receiving step includes the transfer task module 386 selectingone of the unexecuted partial tasks based on execution capabilities ofthe first DST execution unit and the second expected partial taskperformance factor. A third receiving step includes the transfer taskmodule 386 receiving, from the second DST execution unit 374, theselected partial task 398 and a corresponding data group. For example,the transfer task module 386 issues another task information request 402to the second DST execution unit 374, where the other task informationrequest 402 includes a request to transfer the selected partial task398.

The transfer task module 386 further functions to perform a series ofupdating steps. In a first updating step, the transfer task module 386updates the second local task queue by removing the partial task (e.g.,issuing yet another task information request 402 to the second DSTexecution unit 374, where the request includes a task removal request).In a second updating step, the transfer task module 386 updates thesecond expected partial task performance factor based on removing thepartial task. In a third updating step, the transfer task module 386updates the second expected partial task allocation factor based onremoving the partial task.

With regards to facilitating execution of the task by the underutilizedresource, the execute task module 388 executes the allocated partialtask 400 the execute task module 388 executes the allocated partial task400 by a series of execution steps. A first execution step includes theexecute task module 388 updating the first local task queue to includethe allocated partial task 400. A second execution step includes theexecute task module 388 updating the first expected partial taskperformance factor based on the allocated partial task 398. A thirdexecution step includes the execute task module 388 updating the firstexpected partial task allocation factor based on the allocated partialtask 400. A fourth execution step includes the execute task module 388performing the allocated partial task 400 on the corresponding datagroup to produce a partial result 406.

FIG. 41B is a flowchart illustrating an example of transferring apartial task. The method begins at step 410 where a processing module(e.g., of a distributed storage and task (DST) client module of a firstDST execution unit of a distributed computing system) determines thatpartial task processing resources of the first DST execution unit areprojected to be available based on a first local task queue, a firstexpected partial task performance factor, and a first expected partialtask allocation factor. The first DST execution unit is one of a set ofDST execution units, where the set of DST execution units is assigned toperform tasks on large amounts of data. Each of the large amounts ofdata is partitioned into data partitions, where each of the datapartitions is further divided into data groups. Each of the tasks isdivided into a set of partial tasks, and various DST execution units ofthe set of DST execution units are assigned various partial tasks ofvarious ones of the sets of partial tasks to perform on various ones ofthe data groups of various ones of the data partitions of various onesof the large amounts of data.

The determining that the partial task processing resources of the firstDST execution unit are projected to be available includes a series ofdetermining steps. A first determining step includes determining that acurrent snapshot of the first local task queue compares favorably to acurrent queue threshold. A second determining step includes determiningthat a projected snapshot of the first local task queue comparesfavorably to a projected queue threshold that is based on at least oneof the first expected partial task performance factor and the firstexpected partial task allocation factor. When the current snapshot ofthe first local task queue compares favorably to the current queuethreshold and the projected snapshot of the first local task queuecompares favorably to the projected queue threshold, a third determiningstep includes indicating that the partial task processing resources ofthe first DST execution unit are projected to be available.

The method continues at step 412 where the processing module identifiesa second DST execution unit of the set of DST execution units based on acommon task criteria with the first DST execution unit, where the commontask criteria includes one or more of a common site, a common largeamount of data, a common task allocation unit, and a common datapartition. For example, the processing module identifies another DSTexecution unit at common site with the first DST execution unit as thesecond DST execution unit.

The method continues at step 414 where the processing module ascertainsthat partial task processing resources of the second DST execution unitof the set of DST execution units are projected to be overburdened basedon a second local task queue, a second expected partial task performancefactor, and a second expected partial task allocation factor. Theascertaining that the partial task processing resources of the secondDST execution unit of the set of DST execution units are projected to beoverburdened includes a series of ascertaining steps. A firstascertaining step includes ascertaining that a current snapshot of thesecond local task queue compares unfavorably to a current queuethreshold. A second ascertaining step includes ascertaining that aprojected snapshot of the second local task queue compares unfavorablyto a projected queue threshold that is based on at least one of thesecond expected partial task performance factor and the second expectedpartial task allocation factor. When at least one of the currentsnapshot of the second local task queue compares unfavorably to thecurrent queue threshold and the projected snapshot of the second localtask queue compares unfavorably to the projected queue threshold, athird ascertaining step includes indicating that the partial taskprocessing resources of the second DST execution unit are projected tobe overburdened.

The method continues at step 416 where the processing module receives,from the second DST execution unit, a partial task assigned to thesecond DST execution unit in accordance with a partial task allocationtransfer policy to produce an allocated partial task. The receiving thepartial task includes a series of receiving steps. A first receivingstep includes determining unexecuted partial tasks assigned to thesecond DST execution unit. A second receiving step includes selectingone of the unexecuted partial tasks based on execution capabilities ofthe first DST execution unit and the second expected partial taskperformance factor. A third receiving step includes receiving, from thesecond DST execution unit, the selected partial task and a correspondingdata group.

The method continues at step 418 where the processing module updates thesecond local task queue by removing the partial task from the secondlocal task queue (e.g., by issuing a removal request to the second DSTexecution unit). The method continues at step 420 where the processingmodule updates the second expected partial task performance factor basedon removing the partial task. For example, the processing module issuesa update request to the second DST execution unit. As another example,the processing module updates the second expected partial taskperformance factor in a local memory of the first DST execution unit.The method continues at step 422 where the processing module updates(e.g., at least one of the first DST execution unit and the second DSTexecution unit) the second expected partial task allocation factor basedon removing the partial task.

The method continues at step 424 where the processing module executesthe allocated partial task. The executing the partial task includes aseries of executing steps. A first executing step includes updating thefirst local task queue to include the allocated partial task. A secondexecuting step includes updating the first expected partial taskperformance factor based on the allocated partial task. A thirdexecuting step includes updating the first expected partial taskallocation factor based on the allocated partial task. A fourthexecuting step includes performing the allocated partial task on thecorresponding data group to produce a partial result.

FIG. 41C is a schematic block diagram of another embodiment of adistributed computing system that includes a distributed storage andtask (DST) execution unit set 430. The DST execution unit set 430includes a set of DST execution units 432, 434, through 436. A first DSTexecution unit 432 includes a computing device 438. The computing device438 may be implemented utilizing at least one of a server, a storageunit, a storage server, a storage module, a dispersed storage (DS)processing unit, a DS unit, a DST execution unit, a user device, a DSTprocessing unit, and a DST processing module. The computing device 438includes a DS module 440. The DS module 440 may be implemented utilizingat least one of a processing module, one or more central processing unitcores, a software algorithm, a DST client module, and a DS processingmodule. The DS module 440 includes a receive assignment module 442, anidentify underutilized resource module 444, an identify overburdenedresource module 446, and a task module 448.

The set of DST execution units 370 is assigned to perform tasks on largeamounts of data. The large amount of data is divided into a plurality ofdata partitions. Each data partition is dispersed storage error encodedto produce a set of data slice groups. A first sub-set of the set ofdata slice groups includes contiguous data slice groups and a secondsub-set of the set of data slice groups includes error coded data slicegroups. In such a system, a high degree of variance between expectedtask execution and actual task execution may occur when an abundance ofvariability exists with regards to at least one of the initial taskassignment (e.g., randomness from externally assigned tasks) and taskscheduling (e.g., having a central function). The system functionsinclude receiving assignment of partial tasks, identifying anunderutilized resource, identifying an overburdened resource, andfacilitating transfer of a task from the overburdened resource to theunderutilized resource for execution to provide a task executionefficiency improvement for the system.

With regards to receiving assignment of partial tasks, the receiveassignment module 442 receives assignment 451 of executing first partialtasks on first contiguous data slice groups of the first partition ofthe plurality of data partitions. With regards to identifying theunderutilized resource, the identify underutilized resource module 444performs a series of identifying steps. In a first identifying step, theidentify underutilized resource module 444 determines a first expectedpartial task performance factor based on a comparison of the firstpartition of the plurality of data partitions to the plurality of datapartitions. For example, the identify underutilized resource module 444determines a percentage of contiguous data slice groups assigned thefirst DST execution unit 432. As such, the partial task processingresources of the first DST execution unit 432 are projected to be moreavailable when the determined percentage is lower than average and/orlower than a percentage threshold. In a second identifying step, theidentify underutilized resource module 444 determines that partial taskprocessing resources of the first DST execution unit 432 are projectedto be available based on the assignment of the first contiguous dataslice groups, the first expected partial task performance factor, and afirst expected partial task allocation factor (e.g., a projection basedon historical assignments) to produce an availability indication 450.

With regards to identifying the overburdened resource, the identifyoverburdened resource module 446 ascertains that partial task processingresources of a second DST execution unit 434 of the set of DST executionunits 430 are projected to be overburdened based on assignment of secondcontiguous data slice groups assigned to the second DST unit 434, asecond expected partial task performance factor, and a second expectedpartial task allocation factor to produce an overburdened indication452. The second DST execution unit 434 is assigned to execute secondpartial tasks on second contiguous data slice groups of the secondpartition of the plurality of data partitions. The ascertaining includesissuing a performance request 454 to the second DST execution unit 434and receiving a performance response 456 from the second DST executionunit 434. The performance response 456 includes one or more ofassignment of second contiguous data slice groups assigned to the secondDST unit 434, the second expected partial task performance factor, andthe second expected partial task allocation factor. The identifyoverburdened resource module 446 determines the second expected partialtask performance factor based on a comparison of a second partition ofthe plurality of data partitions to the plurality of data partitions.Alternatively, the identify overburdened resource module 446 receivesthe second expected partial task performance factor from the second DSTexecution unit 434.

With regards to facilitating transfer of the task from the overburdenedresource to the underutilized resource for execution, the task module448 receives, from the second DST execution unit 434, a partial task 458and a corresponding one of the second contiguous data slice groups inaccordance with a partial task allocation transfer policy and executesthe partial task on the corresponding one of the second contiguous dataslice groups to produce a partial result 466. The task module 448receive receives the partial task by determining that the partial taskregarding the corresponding one of the second contiguous data slicegroups is pending execution by the second DST execution unit andrequesting the partial task 458 and the corresponding one of the secondcontiguous data slice groups. The requesting includes issuing a taskinformation request 462 to the second DST execution unit 434, receivinga task information response 464 that includes a list of partial taskspending execution by the second DST execution unit 434, selecting thepartial task 458, and issuing another task information request 462 thatincludes a request for the partial task 458

FIG. 41D is a flowchart illustrating another example of transferring apartial task. The method begins at step 470 where a processing module(e.g., of a distributed storage and task (DST) client module of a firstDST execution unit of a distributed computing system) receivesassignment of executing first partial tasks on first contiguous dataslice groups of a first partition of a plurality of data partitions. Thefirst DST execution unit is one of a set of DST execution units. A largeamount of data is divided into the plurality of data partitions and eachdata partition is dispersed storage error encoded to produce a set ofdata slice groups. A first sub-set of the set of data slice groupsincludes contiguous data slice groups and a second sub-set of the set ofdata slice groups includes error coded data slice groups.

The method continues at step 472 where the processing module determinesa first expected partial task performance factor based on a comparisonof the first partition of the plurality of data partitions to theplurality of data partitions (e.g., percentage of contiguous data slicegroups assigned to DST unit, more availability to help execute tasksfrom an overburdened DST execution unit when smaller). The methodcontinues at step 474 where the processing module determines thatpartial task processing resources of the first DST execution unit areprojected to be available based on the assignment of the firstcontiguous data slice groups, the first expected partial taskperformance factor, and a first expected partial task allocation factor.

The method continues at step 476 where the processing module ascertainsthat partial task processing resources of a second DST execution unit ofthe set of DST execution units are projected to be overburdened based onassignment of second contiguous data slice groups assigned to the secondDST unit, a second expected partial task performance factor, and asecond expected partial task allocation factor. The processing moduledetermines the second expected partial task performance factor based ona comparison of a second partition of the plurality of data partitionsto the plurality of data partitions (e.g., more overburdened whenpercentage is higher), where the second DST execution unit is assignedto execute second partial tasks on second contiguous data slice groupsof the second partition of the plurality of data partitions.

The method continues at step 478 where the processing module receives,from the second DST execution unit, a partial task and a correspondingone of the second contiguous data slice groups in accordance with apartial task allocation transfer policy. The receiving the partial taskincludes determining that the partial task regarding the correspondingone of the second contiguous data slice groups is pending execution bythe second DST execution unit and requesting the partial task and thecorresponding one of the second contiguous data slice groups. The methodcontinues at step 480 where the processing module executes the partialtask on the corresponding one of the second contiguous data slicegroups.

FIG. 42 is a flowchart illustrating another example of acquiring a task.The method begins with step 482 where a processing module (e.g., of adistributed storage and task (DST) execution unit) determines anincremental partial task execution capacity level (e.g., based on one ormore of currently scheduled tasks, performance history, an estimate offuture task assignments). When the incremental partial task executioncapacity level is above a threshold, the method continues at step 484where the processing module selects one or more slices of one or moreslice groups stored at a source DST execution unit associated with apartial task execution level below a low threshold. The method continuesat step 486 where the processing module identifies partial tasksassociated with the one or more slices of one or more slice groups. Themethod continues at step 488 where the processing module facilitatesretrieving the partial tasks. The facilitating includes at least one ofsending a partial task retrieval request to the source DST executionunit, retrieving the partial tasks from a DST client module, andobtaining DST allocation information.

The method continues at step 490 where the processing module identifiesat least a decode threshold number of other DST execution unitsassociated with the source DST execution unit (e.g., other pillars of apillar width number of DST execution units). The identifying includes atleast one of retrieving DST allocation information, a storage locationtable lookup, and retrieving slice grouping information. The methodcontinues at step 492 where the processing module generates at least adecode threshold number of partial slice requests. Each request includesone or more of a partial slice identifier (ID), a locally stored sliceID, an encoding matrix, a square matrix, and pillar numbers associatedwith the decode threshold number of DST execution units.

The method continues at step 494 where the processing module sends theat least a decode threshold number of partial slice requests to the atleast a decode threshold number of other DST execution units. Each DSTexecution unit produces a partial slice by obtaining an encoding matrixutilized to generate a desired slice (e.g., extract from the partialrequest, retrieve from a memory), reducing the encoding matrix toproduce a square matrix that exclusively includes rows identified in thepartial request (e.g., slice pillars associated with participating unitsof a decode threshold number of units), inverting the square matrix toproduce an inverted matrix (e.g. alternatively, may extract the invertedmatrix from the partial request), matrix multiplying the inverted matrixby a corresponding locally stored slice to produce a vector, and matrixmultiplying the vector by a row of the encoding matrix corresponding tothe desired slice to be partial encoded (e.g. alternatively, may extractthe row from the partial request), to produce the partial slice (e.g.,desired slice to be built as identified in the partial request).

The method continues at step 496 where the processing module receives adecode threshold number of partial slices. The method continues at step498 where the processing module decodes the decode threshold number ofpartial slices to produce the one or more slices of one or more slicegroups. For example, the processing module decodes the decode thresholdnumber of partial slices by performing a logical exclusive OR (XOR) oneach of the partial slices to produce the one more slices. The methodcontinues at step 500 where the processing module updates DST allocationinformation to indicate that the source DST execution unit is notaffiliated with the partial tasks. The method continues at step 502where the processing module deletes the partial tasks and the one ormore slices of the one or more slice groups when the partial tasks arefavorably executed.

FIG. 43 is a flowchart illustrating an example of balancing tasks, whichincludes similar steps to FIG. 5. The method begins with step 126 ofFIG. 5 where a processing module (e.g., of a distributed storage andtask (DST) client module) receives data and a corresponding task. Themethod continues at step 504 where the processing module identifies atleast one site that includes two or more DST execution units. Theidentifying include one or more of accessing a system registry,accessing system topology information, accessing DST allocationinformation, and accessing a DST network address to physical locationtable.

The method continues at step 506 where the processing module determinesa number of DST execution units to support the task, where at least onesite includes two or more DST execution units. For example, theprocessing module matches a number of expected partial tasks of the taskto the two or more DST execution units of the at least one site (e.g., afavorable total execution capacity level to support execution of theexpected partial tasks). The method continues at step 508 where theprocessing module determines processing parameters of the data based ona number of sites. For example, processing module selects a commonpillar for the two or more DST execution units of the at least one site.The method continues with step 134 of FIG. 5 where the processing moduleprocesses the data in accordance with the processing parameters toproduce slice groupings.

The method continues at step 510 where the processing module determinestask partitioning based on the DST execution units, the number of sites,and the data processing parameters. For example, the processing modulepartitions the tasks in accordance with task execution capacity levelsof the two or more DST execution units. As another example, theprocessing module partitions the tasks such that a common task isassigned to each of the two or more DST execution units per site (e.g.,executing partial tasks on slices of a common pillar). The methodcontinues with step 136 of FIG. 5 where the processing module partitionsthe task based on the task partitioning to produce partial tasks.

The method continues at step 512 where the processing module identifiesslice sub-groupings of each slice grouping associated with the partialtasks. For example, the processing module partitions a slice groupinginto some groupings, wherein a sub-grouping is assigned to DST executionunits at a same site as other sub groupings. The method continues atstep 514 where the processing module sends the slice sub-groupings andcorresponding partial tasks to respective DST execution units. Forexample, the processing module sends slices of a common pillar to thetwo or more DST execution units of the common site.

FIG. 44 is a flowchart illustrating another example of balancing tasks,which includes similar steps of FIGS. 5 and 43. The method begins withstep 126 of FIG. 5 where a processing module (e.g., of a distributedstorage and task (DST) client module) receives data and a correspondingtask. The method continues with steps 504, 506, and 508 of FIG. 43 wherethe processing module identifies at least one site that includes two ormore DST execution units, determines a number of DST execution units tosupport the task, where at least one site includes two or more DSTexecution units, and determines processing parameters of the data basedon a number of sites. The method continues with step 134 of FIG. 5 wherethe processing module processes the data in accordance with theprocessing parameters to produce slice groupings. The method continueswith step 510 of FIG. 43 where the processing module determines taskpartitioning based on the DST execution units, the number of sites, andthe data processing parameters. The method continues with step 136 ofFIG. 5 where the processing module partitions the task based on the taskpartitioning to produce partial tasks.

The method continues at step 516 where the processing module sends theslice groupings and corresponding partial tasks to respective DSTexecution units, where at least one slice grouping is sent to two ormore DST execution units a common site. The sending includes selecting aslice grouping and sending the slice grouping to the respective two ormore DST execution units at the common site. The selecting includesidentifying slices of a common pillar. The sending includes sending theslice grouping to a first DST execution unit at the common site, sendingthe slice grouping to a second DST execution unit at the common site,and sending the slice grouping to the first DST execution unit and thesecond DST execution unit at the common site. The first DST executionunit forwards the slice grouping to other DST execution units of the twoor more DST execution units at the common site when the processingmodule sends the slice grouping to the first DST execution unit. As aresult, the two or more DST execution units receive an identical slicegrouping.

FIG. 45 is a flowchart illustrating another example of balancing tasks.The method begins at step 518 where a processing module (e.g., of adistributed storage and task (DST) execution unit) determines anincremental partial task execution capacity level of a DST executionunit. When the incremental partial task execution capacity level isabove a threshold, the method continues at step 520 where the processingmodule selects one or more locally stored slices only slice group of anassociated common pillar that are also stored in another DST executionunit at a common site. The selecting includes obtaining a partial taskto slice identifier (ID) list (e.g., of DST allocation information) andidentifying the one or more locally stored slices of a slice group of anassociated common pillar based on the list. Alternatively, theprocessing module may generate and send a partial task execution requestto execute more partial tasks of more slices that are already stored inthe DST execution unit.

The method continues at step 522 where the processing module obtainspartial tasks associated with the one or more locally stored slices. Theobtaining includes at least one of sending a partial task retrievalrequest to the other DST execution unit, retrieving the partial tasksfrom a DST client module, and retrieving DST allocation information toidentify the partial tasks. The method continues at step 524 where theprocessing module updates DST allocation information to indicate thatthe other DST execution unit is not affiliated with the partial tasks.The method continues at step 526 where the processing module facilitatesexecution of the partial tasks on the one or more locally stored slicesto produce partial results. The facilitating includes retrieving the oneor more locally stored slices and performing the partial tasks toproduce the partial results. The method continues at step 528 where theprocessing module sends the partial results on behalf of the other DSTexecution unit. For example, the processing module sends the partialresults to a corresponding DST client module. As another example, theprocessing module sends the partial results to the other DST executionunit.

FIG. 46A is a flowchart illustrating an example of determining, whichincludes similar steps to FIG. 5. The method begins with steps 126 and128 of FIG. 5 where a processing module (e.g., of a distributed storageand task (DST) client module) receives data and a corresponding task anddetermines a number of DST execution units to support the task. Themethod continues at step 530 where the processing module determines aslice grouping approach such that each DST execution unit is associatedwith no more than two pillars of slices. For example, the processingmodule determines the slice grouping approach such that a first DSTexecution unit is assigned to store slice groupings associated with afirst pillar and a fourth pillar, a second DST execution unit isassigned to store slice groupings associated with a second pillar and afifth pillar, a third DST execution unit is assigned to store slicegroupings associated with a third pillar and a sixth pillar, a fourthDST execution unit is assigned to store slice groupings associated withthe fourth pillar and the first pillar, a fifth DST execution unit isassigned to store slice groupings associated with the fifth pillar andthe second pillar, and a sixth DST execution unit is assigned to storeslice groupings associated with the sixth pillar and the third pillar.

The method continues at step 532 where the processing module determinesprocessing parameters of the data based on the number of DST executionunits and the slice grouping approach, where the parameters include apillar width that is twice a decode threshold number. For example, theprocessing module determines the decode threshold number as three andthe pillar width at six. As such, each decode threshold number ofnon-encoded slices is paired with a corresponding encoded slices ofremaining slices of a pillar width number of a set of slices. Forinstance, each of three non-encoded slices are paired with each of threecorresponding encoded slices such that slices of pairs of pillars arestored by a pair of DST execution units (e.g., each DST execution unitstores slices associated with two pillars).

The method continues at step 534 where the processing module processesthe data in accordance with the processing parameters to produce slicegroupings in accordance with the slice grouping approach. The methodcontinues with steps 132 and 136 of FIG. 5 where the processing moduledetermines task partitioning based on the DST execution units and thedata processing parameters and partitions the task based on the taskpartitioning to produce partial tasks. The method continues at step 536where the processing module sends the slice groupings and correspondingpartial tasks to respective DST execution units in accordance with theslice grouping approach.

FIG. 46B is a diagram illustrating an example of a dispersed storage andtask execution unit to pillar mapping. The mapping may be utilized toproduce a slice grouping approach. The mapping includes a distributedstorage and task (DST) execution unit to pillar mapping field 540 and anassociated chunk set field 538. The DST execution unit to pillar mappingfield 540 includes a pillar entry for each DST execution unit of a setof DST execution units. The pillar entry includes an indication of apillar identifier (ID) associated with each DST execution unit of a setof DST execution units based on a corresponding chunkset ID. Forexample, a first DST execution unit is assigned to store slice groupingsassociated with a first pillar for chunksets 1, 3, and 4, and a fourthpillar for chunkset 2; a second DST execution unit is assigned to storeslice groupings associated with a second pillar for chunksets 1, 2, and4, and a fifth pillar for chunkset 3; a third DST execution unit isassigned to store slice groupings associated with a third pillar forchunksets 1-3, and a sixth pillar for chunkset 4; a fourth DST executionunit is assigned to store slice groupings associated with the fourthpillar for chunksets 1, 3, and 4, and the first pillar for chunkset 2; afifth DST execution unit is assigned to store slice groupings associatedwith the fifth pillar for chunksets 1, 2, and 4, and the second pillarfor chunkset 3; and a sixth DST execution unit is assigned to storeslice groupings associated with the sixth pillar for chunksets 1-3, andthe third pillar for chunkset 4.

FIG. 47A is a diagram of an example embodiment of a distributed storageand task (DST) unit 550 that includes a controller 86, a memory 88, adistributed task (DT) execution module A, a DT execution module B, and aDST client module 34. The DT execution module A and DT execution moduleB may be implemented utilizing one or more modules. The DST clientmodule 34 includes at least one of an inbound DST processing 82 and anoutbound DST processing. The DST unit 550 ingests raw data 552 of largeamounts of data for storage and processing in accordance with a receivedtask 94. The task 94 includes one or more of a raw data search task anda partial task for execution on slices sent to the DST unit 550 (e.g.,storage and/or processing).

The controller 86 produces control information based on the task 94 tocontrol one or more of the memory 88, DT execution module A, DTexecution module B, and the DST client module 34. For example, thecontroller 86 produces a memory control 174 such that the memory 88caches the raw data 552 and generates index generation task information558 such that DT execution module A processes the raw data 552 inaccordance with the index generation task information 558 to produce adata index 554. The index generation task information 558 includes oneor more of a search parameter, a keyword, pattern recognitioninformation, and timing information. The data index 554 includesmetadata of the raw data 552 including one or more of keywords, dates,internet protocol addresses, partial content, word counts, statistics, asummary, a distributed storage and task network (DSTN) addresscorresponding to raw data storage, a DSTN address corresponding to dataindex storage, and a DSTN address corresponding to index data storage.

The controller 86 may also generate data indexing task information 560with regards to indexing of the data index 554. The data indexing taskinformation 560 includes one or more of data reduction instructions, akeyword filter, a data index reference, and a indexed data format. TheDT execution module B processes the raw data of 552 in accordance withthe data indexing task information 560 to produce indexed data 556. Theindexed data 556 includes a subset of the raw data 552 organized inaccordance with the data index 554.

The controller 86 controls the memory 88 with the memory control 174 tofacilitate caching one or more of the raw data 552, the data index 554,and the indexed data 556. The memory control 174 may also facilitate thememory 88 outputting one or more of the raw data 552, the data index554, and the indexed data 556. The memory control 174 may alsofacilitate the memory 88 inputting slice groupings 96 for caching in thememory 88 to facilitate further processing by DT execution module Aand/or B.

The controller 86 generates and outputs a DST control 178 to the DSTclient module 34 to facilitate the generation and outputting of one ormore of slice groupings 96 of the raw data 552, of the data index 554,of the indexed data 556, one or more partial tasks 98, and slicegroupings that includes partial redundancy data with respect to raw data552 that is stored as data block groupings in memory 88. For example,the DST client module 34 sends a portion of the slice groupings 96 ofthe raw data 552 to the memory 88 for storage and sends other portionsof the slice groupings 96 to other DST units for storage therein. Asanother example, the DST client module 34 generates slice groupings 96of the indexed data 556 and sends the slice groupings 96 of indexed data556 to at least one other DST unit for further processing (e.g., apattern search). As yet another example, the DST client module 34generates slice groupings 96 to include partial redundancy data for eachrespective redundancy DST unit and outputs corresponding partialredundancy data to each respective redundancy DST unit. The generatingof the partial redundancy data is discussed in greater detail withreference to FIGS. 47B-47F.

FIG. 47B is a schematic block diagram of an example of a dispersedstorage network that includes a dispersed storage (DS) processing unit562 and a set of DS units 564. Alternatively, the DS processing unit 562may include a distribute storage and task (DST) processing unit and eachDS unit 564 may include a DST execution unit. The network functions toingest large amounts of data 1-3 for storage in the set of DS units 564.The DS processing unit 562 encodes data 1-3 using a dispersed storageerror coding function to produce a plurality of sets of encoded dataslices, groups the encoded data slices of the plurality of sets ofencoded data slices to produce data block 1-3 slice groupings and errorencoded data block 4-5 slice groupings, outputs the data block 1-3 slicegroupings to DS units 564 that are associated with storing data, andoutputs error encoded data block 4-5 slice groupings to the other DSunits 564 that are associated with storing redundancy data (e.g., errorcoded slices). The network utilizes a centralized data ingestionapproach by utilizing the DS processing unit 562 to ingest large amountsof data, enables execution of partial tasks by the DS units 564 is thatare associated with storing the data, and enables improved storagereliability via utilization of the DS units 564 that are associated withstoring redundancy data.

FIG. 47C is a schematic block diagram of another example of a dispersedstorage network that includes a set of dispersed storage (DS) units 566.Alternatively, each DS unit 566 may be implemented utilizing adistributed storage and task (DST) execution unit. A decode thresholdnumber of DS units 566 of the set of DS units 566 are associated withstoring data and a difference number between a pillar width and thedecode threshold number of remaining DS units 566 are associated withstoring redundancy data. As contrasted to the network depicted in FIG.47B, the network depicted in FIG. 47C utilizes a decentralized dataingestion approach by utilizing a decode threshold number of DS units566 of the set of DS units 566 to ingest large amounts of data (e.g.,data 1-3). The network enables execution of partial tasks by the DSunits 566 that are associated with storing the data and enables improvedstorage reliability via utilization of the DS units 566 that areassociated with storing redundancy data.

Each DS unit 566 of the decode threshold number of DS units 566associated with storing data receives a portion of the large amounts ofdata for local storage and further processing. For example, a first DSunit 566 of the decode threshold number of DS units 566 receives data 1,etc. The further processing includes partitioning the portion of thelarge amount of data to produce a plurality of data partitions, storinga data block slice grouping for each of the data partitions where thedata block slice grouping corresponds to the DS unit 566 of the decodethreshold number of DS units 566, generating partial error recoveryinformation based on a corresponding data block slice grouping for eachDS unit 566 of the DS units 566 that are associated with storingredundancy data, and outputting corresponding partial error recoveryinformation to each DS unit 566 of the DS units 566 that associated withstoring redundancy data.

Each DS unit 566 of the decode threshold number of DS units 566generates the partial error recovery information (PERI) for each DS unit566 of the remaining DS units 566 associated with storing redundancydata by a series of steps. For example, in a first step, the first DSunit 566 generates partial error recovery information for a fourth DSunit 566 that is associated with storing redundancy data with respect tothe first DS unit 566 as PERI (4,1); and, in a second step, the first DSunit 566 generates partial error recovery information for a fifth DSunit 566 that is associated with storing redundancy data with respect tothe first DS unit 566 as PERI (5,1) etc. when a decode threshold isthree and a pillar width is five.

Each DS unit 566 of the DS units 566 associated with storing redundancydata receives partial error recovery information from each DS unit 566of the decode threshold number of DS units 566 associated with storingdata and generates corresponding respective redundancy data for storagewithin the DS unit 566. In an example of operation, a first stepincludes the fourth DS unit 566 receiving the PERI (4, 1) from the firstDS unit 566 and locally storing the PERI (4, 1). A second step includesthe fourth DS unit 566 receiving a PERI (4, 2) from a second DS unit566. A third step includes the fourth DS unit 566 performing a updatingfunction (e.g., exclusive OR) on the PERI (4, 1) and utilizing the PERI(4, 2) to produce a temporary error coded data slice grouping. A fourthstep includes the fourth DS unit 566 receiving a PERI (4, 3) from athird DS unit 566. A fifth step includes performing the updatingfunction on the temporary error coded data slice grouping utilizing thePERI (4, 3) to produce the respective redundancy data that includes acompleted error coded data slice grouping. A sixth step includes storingthe completed error coded data slice grouping. The method of operationof the network is discussed in greater detail with reference to FIGS.47D-47F.

FIG. 47D is a flowchart illustrating an example of securely and reliablystoring data. The method begins at step 568 where ingesting dispersedstorage (DS) units of a set of DS units store respective portions of alarge amount of data based on a data partitioning agreement of the setof DS units. The large amounts of data includes one or more real timedata, multiple video streams, traffic on internet, company wide datatraffic, etc. The data partitioning agreement includes at least one ofan indication of a dispersed storage error coding function, anaddressing scheme for storing the respective portions of the largeamount of data, a data segment size indication, an indicator for datablock size and data block quantity per data segment, a number ofingesting DS units, a number of redundancy DS units, and a logicaldivision of the large amount of data to identify the respective portions(e.g., geographic location, timestamp, internet protocol address range,chapters, streaming video sources, source identifiers, etc.).

The method continues at step 570 where each of the ingesting DS unitsgenerates first respective partial redundancy data and second respectivepartial redundancy data for the respective portion of the large amountof data. Alternatively, each of the ingesting DS units generates apillar width number minus a decode threshold number of respectivepartial redundancy data for the respective portion of the large amountof data. For example, each ingesting DS unit generates the firstrespective partial redundancy data and the second respective partialredundancy data when the pillar width is five and the decode thresholdis three.

The generating includes at least one of a variety of generatingapproaches. In a first generating approach, a first ingesting DS unit ofthe ingesting DS units generates the first and second respective partialredundancy data of the first ingesting DS unit by a series of generatingsteps. A first generating step includes dividing a respective portion ofthe large amounts of data into a plurality of data segments inaccordance with the data partitioning agreement. A second generatingstep includes, for each of the plurality of data segments, a series ofsub-generating steps. A first sub-generating step includes dividing acurrent data segment into a set of data blocks in accordance with thedata partitioning agreement. A second sub-generating step includesarranging the set of data blocks in a single row data matrix. A thirdsub-generating step includes multiplying the single row data matrix by afirst value of a first error encoding row of an encoding matrix toproduce first partial redundancy data for the current data segment. Afourth sub-generating step includes multiplying the single row datamatrix by a second value of the first error encoding row to producesecond partial redundancy data for the current data segment. The seriesof generating steps continues with a third generating step that includescombining the first partial redundancy data for each of the current datasegments to produce the first respective redundancy data of the firstingesting DS unit. A fourth generating step includes combining thesecond partial redundancy data for each of the current data segments toproduce the second respective redundancy data of the first ingesting DSunit.

In the first generating approach, a second ingesting DS unit of theingesting DS units generates the first and second respective partialredundancy data of the second ingesting DS unit by a series ofgenerating steps. A first generating step includes dividing a secondrespective portion of the large amounts of data into a plurality of datasegments in accordance with the data partitioning agreement. A secondgenerating step includes, for each of the plurality of data segments, aseries of sub-generating steps. A first sub-generating step includesdividing a current data segment into a set of data blocks in accordancewith the data partitioning agreement. A second sub-generating stepincludes arranging the set of data blocks in a single row data matrix. Athird sub-generating step includes multiplying the single row datamatrix by a first value of a second error encoding row of the encodingmatrix to produce first partial redundancy data for the current datasegment. A fourth sub-generating step includes multiplying the singlerow data matrix by a second value of the second error encoding row toproduce second partial redundancy data for the current data segment. Theseries of generating steps continues with a third generating step thatincludes cumulating the first partial redundancy data for each of thecurrent data segments to produce the first respective redundancy data ofthe second ingesting DS unit. A fourth generating step includescumulating the second partial redundancy data for each of the currentdata segments to produce the second respective redundancy data of thesecond ingesting DS unit.

In a second generating approach, the first ingesting DS unit of theingesting DS units generates the first and second respective partialredundancy data of the first ingesting DS unit by a series of generatingsteps. A first generating step includes dividing the respective portionof the large amounts of data into the plurality of data segments inaccordance with the data partitioning agreement. A second generatingstep includes, for each of the plurality of data segments, a series ofsub-generating steps. A first sub-generating step includes dividing acurrent data segment into the set of data blocks in accordance with thedata partitioning agreement. A second sub-generating step includesexclusive ORing a first sub-set of the data blocks to produce firstpartial redundancy data for the current data segment. A thirdsub-generating step includes exclusive ORing a second sub-set of thedata blocks to produce second partial redundancy data for the currentdata segment. The series of generating steps continues with a thirdgenerating step that includes combining the first partial redundancydata for each of the current data segments to produce the firstrespective redundancy data of the first ingesting DS unit. A fourthgenerating step includes combining the second partial redundancy datafor each of the current data segments to produce the second respectiveredundancy data of the first ingesting DS unit.

In the second generating approach, the second ingesting DS unit of theingesting DS units generates the first and second respective partialredundancy data of the second ingesting DS unit by a series ofgenerating steps. A first generating step includes dividing therespective portion of the large amounts of data into the plurality ofdata segments in accordance with the data partitioning agreement. Asecond generating step includes, for each of the plurality of datasegments, a series of sub-generating steps. A first sub-generating stepincludes dividing a current data segment into a set of data blocks inaccordance with the data partitioning agreement. A second sub-generatingstep includes exclusive ORing a first sub-set of the data blocks toproduce first partial redundancy data for the current data segment. Athird sub-generating step includes exclusive ORing a second sub-set ofthe data blocks to produce second partial redundancy data for thecurrent data segment. The series of generating steps continues with athird generating step that includes combining the first partialredundancy data for each of the current data segments to produce thefirst respective redundancy data of the second ingesting DS unit. Afourth generating step includes combining the second partial redundancydata for each of the current data segments to produce the secondrespective redundancy data of the second ingesting DS unit.

The method continues at step 572 where each of the ingesting DS unitssends the first respective partial redundancy data to a first redundancyDS unit of the set of DS units. The method continues at step 574 whereeach of the ingesting DS unit sends the second respective partialredundancy data to a second redundancy DS unit of the set of DS units.The method continues at step 576 where the first redundancy DS unitgenerates first respective redundancy data based on the first respectivepartial redundancy data of each of the ingesting DS units. The firstredundancy DS unit performs a first portion of a dispersed storage errorencoding function (e.g., exclusive OR, a Galois field mathematicalfunction, etc.) to combine the first respective partial redundancy dataof each of the ingesting DS units to generate the first respectiveredundancy data. The method continues at step 578 where the firstredundancy DS unit stores the first respective redundancy data.

The method continues at step 580 where the second redundancy DS unitgenerates second respective redundancy data based on the secondrespective partial redundancy data of each of the ingesting DS units.The second redundancy DS unit performs a second portion of the dispersedstorage error encoding function to combine the second respective partialredundancy data of each of the ingesting DS units to generate the secondrespective redundancy data. The method continues at step 582 where thesecond redundancy DS unit stores the second respective redundancy data.

Still further ingesting DS units of the set of DS units may be utilizedto store further large amounts of data. When storing further largeamounts of data, the method continues at step 584 where second ingestingDS units of the set of DS units stores portions of a second large amountof data based on a second data partitioning agreement of the set of DSunits. The method continues at step 586 where each of the secondingesting DS units generates another first respective partial redundancydata and another second respective partial redundancy data for therespective portion of the second large amount of data. The methodcontinues at step 588 where each of the second ingesting DS units sendsthe other first respective partial redundancy data to another firstredundancy DS unit of the set of DS units. The method continues at step590 where each of the second ingesting DS unit sends the other secondrespective partial redundancy data to another second redundancy DS unitof the set of DS units.

The method continues at step 592 where the other first redundancy DSunit generates other first respective redundancy data based on the otherfirst respective partial redundancy data of each of the second ingestingDS units. The method continues at step 594 where the other firstredundancy DS unit stores the other first respective redundancy data.The method continues at step 596 where the other second redundancy DSunit generates other second respective redundancy data based on theother second respective partial redundancy data of each of the secondingesting DS units. The method continues at step 598 where the othersecond redundancy DS unit stores the other second respective redundancydata. The method of operation of the set of DS units is discussed ingreater detail with reference to FIGS. 47E and 47F.

FIG. 47E is a schematic block diagram of another example of a dispersedstorage network that includes a dispersed storage (DS) unit set 600. TheDS unit set 600 includes a DS unit 602, redundancy DS units 604-606, andingesting DS units 608. At least one of the redundancy DS units 604-606and ingesting DS units 608 includes the DS unit 602. The DS unit set 600includes at least a pillar width number of DS units including at least adecode threshold number of ingesting DS units. The DS unit 602 includesa computing device 610. The computing device 610 may be implementedutilizing at least one of a server, a storage unit, a storage server, astorage module, a dispersed storage (DS) processing unit, a DS unit, adistributed storage and task (DST) execution unit, a user device, a DSTprocessing unit, and a DST processing module. The computing device 610includes a DS module 612 and a memory 614. The memory 614 may beimplemented utilizing one or more of a memory device, a memory module,an optical memory, a magnetic memory, a solid-state memory, and astorage server. The DS module 612 may be implemented utilizing at leastone of a processing module, one or more central processing unit cores, asoftware algorithm, a DST client module, and a DS processing module. TheDS module 612 includes a determine unit type module 616, an ingestmodule 618, and a redundancy module 620.

The DS unit 602 functions include identifying a function type 622 of theDS unit 602 (e.g., ingesting DS unit, redundancy DS unit), ingestingdata, and creating redundancy data. With regards to the identifying thefunction type 622 of the DS unit 602, the determine unit type module 616determines whether the DS unit 602 is an ingesting DS unit or aredundancy DS unit of the set of DS units 600 based on a datapartitioning agreement of the set of DS units 600 (e.g., role may changebetween DS units for different portions data for ingestion) to producethe function type 622.

With regards to the ingesting the data, the ingest module 618, when theDS unit 602 is the ingesting DS unit, performs a series of ingestingsteps. In a first ingesting step, the ingest module 618 stores arespective portion 622 of a large amount of data in accordance with thedata partitioning agreement of the set of DS units 600 in the memory614. In a second ingesting step, the ingest module 618 generates firstand second respective partial redundancy data 626-628 based on therespective portion 624 of the large amounts of data. The ingest module618 generates the first and second respective partial redundancy data626-628 utilizing a variety of generating partial redundancy dataapproaches.

In a first generating partial redundancy data approach, the ingestmodule 618 performs a series of approach generating steps. A firstapproach generating step includes the ingest module 618 dividing therespective portion 624 of the large amounts of data into a plurality ofdata segments in accordance with the data partitioning agreement. Asecond approach generating step includes, for each of the plurality ofdata segments, the ingest module 618 performing a series ofsub-generating steps. A first sub-generating step includes the ingestmodule 618 dividing a current data segment into a set of data blocks inaccordance with the data partitioning agreement. A second sub-generatingstep includes the ingest module 618 arranging the set of data blocks ina single row data matrix. A third sub-generating step includes theingest module 618 multiplying the single row data matrix by a firstvalue of a first error encoding row of an encoding matrix to producefirst partial redundancy data for the current data segment. A fourthsub-generating step includes the ingest module 618 multiplying thesingle row data matrix by a second value of the first error encoding rowto produce second partial redundancy data for the current data segment.The series of approach generating steps continues with a third approachgenerating step that includes the ingest module 618 combining the firstpartial redundancy data for each of the current data segments to producethe first respective redundancy data 626. A fourth approach generatingstep includes the ingest module 618 combining the second partialredundancy data for each of the current data segments to produce thesecond respective redundancy data 628.

In a second generating partial redundancy data approach, the ingestmodule 618 performs a series of alternative approach generating steps. Afirst alternative approach generating step includes, the ingest module618 dividing the respective portion 624 of the large amounts of datainto the plurality of data segments in accordance with the datapartitioning agreement. A second alternative approach generating stepincludes, for each of the plurality of data segments, the ingest module618 performing a series of sub-generating steps. A first sub-generatingstep includes the ingest module 618 dividing a current data segment intothe set of data blocks in accordance with the data partitioningagreement. A second sub-generating step includes the ingest module 618exclusive ORing a first sub-set of the data blocks to produce firstpartial redundancy data for the current data segment. A thirdsub-generating step includes the ingest module 618 exclusive ORing asecond sub-set of the data blocks to produce second partial redundancydata for the current data segment. The series of alternative approachgenerating steps continues with a third approach generating step thatincludes the ingest module 618 combining the first partial redundancydata for each of the current data segments to produce the firstrespective redundancy data 626. A fourth approach generating stepincludes the ingest module 618 combining the second partial redundancydata for each of the current data segments to produce the secondrespective redundancy data 628.

The series of ingesting steps continues with a third ingesting step,where the ingest module 618 sends the first respective partialredundancy data 626 to a first redundancy DS unit 604 of the set of DSunits 600. In a fourth ingesting step, the ingest module 618 sends thesecond respective partial redundancy data 628 to a second redundancy DSunit 606 of the set of DS units 600.

With regards to the creating the redundancy data, the redundancy module620, when the DS unit 602 is the redundancy DS unit, generatesrespective redundancy data 632 based on respective partial redundancydata received 630 (e.g., the first respective partial redundancy data626) from each ingesting DS unit 608 of the set of DS units 600 andstores the respective redundancy data 632 in memory 614. The redundancymodule 620 generates the respective redundancy data 632 by receiving therespective partial redundancy data 630 received from each of theingesting DS units 608 of the set of DS units 600 (e.g., first, second,etc.) and performing a respective portion of a dispersed storage errorencoding function to combine the first respective partial redundancydata 626 of each of the ingesting DS units 608 to generate therespective redundancy data 632 (e.g., XOR, Galois field addition, etc.).

FIG. 47F is a flowchart illustrating another example of securely andreliably storing data. The method begins at step 640 where a processingmodule (e.g., of a dispersed storage (DS) unit) determines whether theDS unit is an ingesting DS unit or a redundancy DS unit of a set of DSunits based on a data partitioning agreement of the set of DS units.When the DS unit is the ingesting DS unit, the method continues at step642 where the processing module stores a respective portion of a largeamount of data in accordance with the data partitioning agreement of theset of DS units (e.g., in a locally memory).

The method continues at step 644 where the processing module generatesfirst and second respective partial redundancy data based on therespective portion of the large amounts of data. The generating thefirst and second respective partial redundancy data includes a varietyof generating partial redundancy data approaches. In a first generatingpartial redundancy data approach, the processing module performs aseries of approach generating steps. A first approach generating stepincludes the processing module dividing the respective portion of thelarge amounts of data into a plurality of data segments in accordancewith the data partitioning agreement. A second approach generating stepincludes, for each of the plurality of data segments, the processingmodule performing a series of sub-generating steps. A firstsub-generating step includes dividing a current data segment into a setof data blocks in accordance with the data partitioning agreement. Asecond sub-generating step includes arranging the set of data blocks ina single row data matrix. A third sub-generating step includesmultiplying the single row data matrix by a first value of a first errorencoding row of an encoding matrix to produce first partial redundancydata for the current data segment. A fourth sub-generating step includesmultiplying the single row data matrix by a second value of the firsterror encoding row to produce second partial redundancy data for thecurrent data segment. The series of approach generating steps continueswith a third approach generating step that includes the processingmodule combining the first partial redundancy data for each of thecurrent data segments to produce the first respective redundancy data. Afourth approach generating step includes the processing module combiningthe second partial redundancy data for each of the current data segmentsto produce the second respective redundancy data.

In a second generating partial redundancy data approach, the processingmodule performs a series of alternative approach generating steps. Afirst alternative approach generating step includes, the processingmodule dividing the respective portion of the large amounts of data intothe plurality of data segments in accordance with the data partitioningagreement. A second alternative approach generating step includes, foreach of the plurality of data segments, the processing module performinga series of sub-generating steps. A first sub-generating step includesdividing a current data segment into the set of data blocks inaccordance with the data partitioning agreement. A second sub-generatingstep includes exclusive ORing a first sub-set of the data blocks toproduce first partial redundancy data for the current data segment. Athird sub-generating step includes exclusive ORing a second sub-set ofthe data blocks to produce second partial redundancy data for thecurrent data segment. The series of alternative approach generatingsteps continues with a third approach generating step that includes theprocessing module combining the first partial redundancy data for eachof the current data segments to produce the first respective redundancydata. A fourth approach generating step includes the processing modulecombining the second partial redundancy data for each of the currentdata segments to produce the second respective redundancy data.

The method continues at step 646 where the processing module sends thefirst respective partial redundancy data to a first redundancy DS unitof the set of DS units. The method continues at step 648 where theprocessing module sends the second respective partial redundancy data toa second redundancy DS unit of the set of DS units.

When the DS unit is the redundancy DS unit, the method continues at step650 where the processing module generates respective redundancy databased on the respective partial redundancy data received from eachingesting DS unit of the set of DS units. The generating the respectiveredundancy data includes receiving the respective partial redundancydata received from each of the ingesting DS units of the set of DS unitsand performing a respective portion of a dispersed storage errorencoding function to combine the first respective partial redundancydata of each of the ingesting DS units to generate the respectiveredundancy data. The method continues at step 652 where the processingmodule stores the respective redundancy data (e.g., in a locallymemory).

FIG. 48A is a schematic block diagram of another example of a dispersedstorage network that includes a set of dispersed storage (DS) units 654that includes at least a decode threshold number of ingesting DS units656 and a number of remaining redundancy DS units 658. Alternatively,the set of DS units 654 may be implemented utilizing one or moredistributed storage and task (DST) execution units. The set of DS units654 receives large amounts of data 660 and stores the large amounts ofdata 660 as stored data block 662. The ingesting DS units 656 of the setof DS units 654 are associated with storing data blocks of data and theredundancy DS units 658 of the set of DS units 654 are associated withstoring error coded data blocks of redundancy data. The set of DS units654 enables execution of partial tasks by the ingesting DS unit 656 onthe data blocks of data, enables improved storage reliability viautilization of the redundancy DS units 658 that are associated withstoring the redundancy data, and enables improved storage efficiency byidentifying and remedying stores data blocks of the stored data blocks662 that are substantially similar.

Each ingesting DS unit 656 receives a respective section (e.g., of adata 1-3) of the large amount of data 660 substantial in parallel,within a common time period (e.g., of t1, t2, t3, etc.), with otheringesting DS units 656 receiving other respective sections of the largeamount of data 660 and divides the respective section to produce one ormore data blocks. For example, during the first time period t1, a firstingesting DS unit 656 receives a data block 1 of data 1, a secondingesting DS unit 656 receives a data block 10 of data 2, and a thirdingesting DS unit 656 receives a data block 2 of data 3.

The ingesting DS unit 656 store (e.g., at least temporarily) the storedata blocks 662 as a plurality of collections (e.g., 1, 2, 3, . . . ) ofdata blocks where each collection includes a decode threshold number ofdata blocks stored in the ingesting DS unit 656 and corresponding errorcoded data blocks in the redundancy DS unit 658. For example, a firstcollection includes data block 1 stored in the first ingesting DS unit656, data block 10 stored in the second ingesting DS unit 656, datablock 2 stored in the third ingesting DS unit 656, an error coded datablock 1-4 stored in a first redundancy DS unit 658, and an error codeddata block 1-5 stored in a second redundancy DS unit 658.

With regards to improved storage reliability, for each collection, eachingesting DS unit 656 generates partial error recovery information(PERI) of data blocks for permanent storage for each redundancy DS unit658 associated with storing redundancy data for the partitions by aseries of steps. In an example of operation, for the first collection, afirst step of a series of steps includes the first ingesting DS unit 656generating partial error recovery information for the first redundancyDS unit 658 that is associated with storing redundancy data with respectto the first ingesting DS unit 656 as PERI (4,1); and, in a second step,the first ingesting DS unit 656 generates partial error recoveryinformation for the second redundancy DS unit 658 that is associatedwith storing redundancy data with respect to the first ingesting DS unit656 as PERI (5,1) etc. when a decode threshold is three and a pillarwidth is five of a dispersed storage error coding function.

The example of operation continues where each redundancy DS unit 658associated with storing redundancy data receives partial error recoveryinformation from each ingesting DS unit 656 associated with storing datablocks of a corresponding collection and generates correspondingrespective redundancy data for storage as error coded data blocks withinthe redundancy DS unit 658. For instance, a first sub-step of a seriesof sub-steps includes the first redundancy DS unit 658 receiving thePERI (4, 1) from the first ingesting DS unit 656 and locally storing thePERI (4, 1). A second sub-step includes the first redundancy DS unit 658receiving a PERI (4, 2) from the second ingesting DS unit 656. A thirdsub-step includes the first redundancy DS unit 658 performing anupdating function (e.g., exclusive OR) on the PERI (4, 1) and utilizingthe PERI (4, 2) to produce a temporary error coded data slice grouping.A fourth sub-step includes the first redundancy DS unit 658 receiving aPERI (4, 3) from the third ingesting DS unit 656. A fifth sub-stepincludes the first redundancy DS unit 658 performing the updatingfunction on the temporary error coded data slice grouping utilizing thePERI (4, 3) to produce respective redundancy data that includes acompleted error coded data slice grouping for the first collection. Asixth sub-step includes the first redundancy DS unit 658 storing thecompleted error coded data slice grouping as the error coded data block1-4 (e.g., in a local memory of the first redundancy DS unit 658).

With regards to enabling improved storage efficiency by identifying andremedying stored data blocks of the stored data blocks 662 that aresubstantially similar, each ingesting DS unit 656 detects thesubstantially similar stored data blocks and facilitates a remedy. Eachingesting DS unit 656 may, from time period to time period, receive adata block that is substantially similar to a data block received byanother ingesting DS unit 656 and/or by the ingesting DS unit 656 duringanother time period. For example, during the second time t2, the firstingesting DS unit 656 receives data block 2 which was previouslyreceived by the third ingesting DS unit 656 during the first time periodt1.

The ingesting DS unit 656 detects the substantially similar stored datablock when a new data block is received and/or by analyzing stored datablocks subsequent to storage of the data blocks and generation andstorage of corresponding error coded data blocks. When detecting storeddata blocks that are substantially similar, the ingesting DS unit 656may determine a number of allowable substantially similar data blocksand identify desired data blocks when a number of substantially similardata blocks is greater than the number of allowable substantiallysimilar data blocks. The identifying the desired data blocks may includeselecting the desire data blocks based on one or more of an associatedingesting DS unit 656, a task execution capability level of theassociated ingesting DS unit 656, a pending partial task for executionon a corresponding one of the substantially similar data blocks, astorage reliability goal, and a task execution goal. For example, thefirst ingesting DS unit 656 does not identify data block 2 as thedesired data block (e.g., rather identifies data block 2 forelimination) and the third ingesting DS unit 656 does identify datablock 2 as the desired data block when the allowable substantiallysimilar data blocks is zero and a task execution capability level of thethird ingesting DS unit 656 compares more favorably to the taskexecution goal than does a task execution capability level of the firstingesting DS unit 656. As another example, the second ingesting DS unit656 identifies data block 18 of collection 3 as a desired substantiallysimilar data block and the third ingesting DS unit 656 identifies datablock 18 of collection to as a desired substantially similar data blockwhen the allowable substantially similar data blocks is at least one.

When the associated ingesting DS unit 656 does not identify thesubstantially similar data block as the desired data block, theassociated ingesting DS unit 656 may replace a temporarily stored datablock for elimination with a data block that is to be storedpermanently. The replacing includes one or more of establishingcorresponding dispersed storage network addresses for the data block tobe stored permanently to be associated with a partition of thetemporarily stored data block for elimination and updating and/orestablishing corresponding error coded data blocks. For example, thefirst ingesting DS unit 656 replaces data block 2 with newly receiveddata block 4 that was not permanently stored and updates error codeddata blocks 2-4 and 2-5 based on a data block 4. The method to identifyand remedy stored data blocks that are substantially similar isdiscussed in greater detail with reference to FIGS. 48B-48E.

FIG. 48B is a schematic block diagram of another example of a dispersedstorage network that includes a dispersed storage (DS) unit set 670. TheDS unit set 670 includes a DS unit 672, redundancy DS units 674-676, andingesting DS units 678. At least one of the ingesting DS units 678includes the DS unit 672. The DS unit set 670 includes at least a pillarwidth number of DS units including at least a decode threshold number ofingesting DS units 672, and 678. The DS unit 672 includes a computingdevice 680. The computing device 680 may be implemented utilizing atleast one of a server, a storage unit, a storage server, a storagemodule, a dispersed storage (DS) processing unit, a DS unit, a DSTexecution unit, a user device, a distributed storage and task (DST)processing unit, and a DST processing module. The computing device 680includes a DS module 682 and a memory 684. The memory 684 may beimplemented utilizing one or more of a memory device, a memory module,an optical memory, a magnetic memory, a solid-state memory, a temporarystorage module, a permanent storage module, and a storage server. The DSmodule 682 may be implemented utilizing at least one of a processingmodule, one or more central processing unit cores, a software algorithm,a DST client module, and a DS processing module. The DS module 682includes an ingest module 686, a determine storage module 688, a storedata module 690, and a store new data module 692.

The set of DS units 670 ingests a large amount of data. The set of DSunits 670 divides large amount of data into a set of partitions anddivides each of the sets of partitions into a set of sections. The DSunit 672 functions include ingesting data of the large amount of data,determining a storage approach for the data, permanently storing thedata, and permanently storing other data. With regards to the ingestingdata, the ingest module 686 ingests a respective section 694 of thelarge amount of data. For example, the ingest module 686 extracts aportion of a partition of the large amount of data in accordance with anextraction approach of the set of DS units 670 and divides the portionof the partition into the respective section 694.

With regards to the determining the storage approach for the data, thedetermine storage module 688, for the ingested respective section 694 ofdata, divides the ingested respective section 694 of data into aplurality of data segments and performs a series of determining storagesteps for a data segment of the plurality of data segments. In a firstdetermining storage step, the determine storage module 688 divides thedata segment into a plurality of data blocks 700 (e.g., a row of a datamatrix). In a second determining storage step, the determine storagemodule 688 temporarily stores the plurality of data blocks 700. Forexample, the plurality of data blocks 700 are stored in a temporarystorage portion of memory 684. In a third determining storage step, thedetermine storage module 688 determines whether to not permanently storeone or more of the plurality of data blocks 700.

The determine storage module 688 determines whether to not permanentlystore the one or more of the plurality of data blocks 700 by a varietyof determining approaches. A determining approach includes the determinestorage module 688 performing a series of determining approach steps. Afirst determining approach step includes the determine storage module688 determining that at least one other DS unit (e.g., of the ingestingDS units 678) of the set of DS units 670 has ingested a respectivesection that is substantially similar to the ingested respective section694. For example, the determine storage module 688 receives respectivesection storage information 702 from the at least one other DS unit thatincludes at least one of a representation (e.g., a hashing functionresult over the respective section of the other DS unit) of therespective section that is substantially similar to the ingestedrespective section 694 and the respective section that is substantiallysimilar to the ingested respective section 694. Next, the determinestorage module 688 performs at least one of two comparisons thatincludes comparing the representation from the other DS unit with arepresentation of the ingested respective section 694 and comparing therespective section from the other unit with the ingested respectivesection 694. In the example, the determine storage module 688 determinesthat the at least one other DS unit has ingested the respective sectionthat is substantially similar to the ingested respective section 694when at least one of the comparisons is favorable (e.g., substantiallythe same). Alternatively, the first determining approach step includesthe determine storage module 688 determining that the DS unit 672 hasingested the respective section that is substantially similar to theingested respective section 694 (e.g., stored temporarily or permanentlyby the DS unit 672).

In response to the determining that the at least one other DS unit hasingested the respective section that is substantially similar to theingested respective section 694, the determine storage module 688performs a second determining approach step that includes a series ofsub-steps. A first sub-step includes the determine storage module 688determining whether a de-duplication function is to be applied to theingested respective section 694. The determining may be based on one ormore of how many respective sections stored by other DS units aresubstantially similar to the ingested respective section 694, a desiredmaximum number of substantially similar ingested respective section 694,identities of the other DS units storing the respective sections thatare substantially similar to the ingested respective section 694, taskexecution performance availability levels of the other DS units, storagecapacity availability levels of the other DS units, a data typeindicator, a lookup, a query, a replication goal, a storage goal,identity of the DS unit 672, and a performance goal. For example, thedetermine storage module 688 determines to apply the de-duplicationfunction when another DS unit is associated with a preferred taskexecution performance availability level as compared to a task executionperformance availability level of DS unit 672 and a desired maximumnumber of substantially similar ingested respective sections is zero. Asanother example, the determine storage module 688 determines not toapply the de-duplication function when the task execution performanceavailability level of DS unit 672 compares more favorably to the taskexecution performance availability level of the other DS unit and thedesired maximum number of substantially similar ingested respectivesections is zero. In addition, the determine storage module 688 mayissue a request to the other DS unit to apply the de-duplicationfunction on the corresponding substantially similar ingested respectivesection stored at the other DS unit.

When the de-duplication function is to be applied, a second sub-step ofthe second determining approach step includes the determine storagemodule 688 identifying the one or more of the plurality of data blocks700 that are not to be permanently stored. When the de-duplicationfunction is not to be applied, the second sub-step includes thedetermine storage module 688 indicating that the one or more of theplurality of data blocks 700 are to be permanently stored.

Another determining approach, of the variety of determining approaches,includes the determine storage module 688 performing a series ofalternate determining approach steps. A first alternate determiningapproach step includes the determine storage module 688 analyzing theingested respective section 694 in accordance with data analysiscriteria. The data analysis criteria includes one or more of a datasize, a data type, a data priority level, identification of anassociated partial task, a data content indicator, and a pattern match.For example, the determine storage module 688 attempts to match apattern to the ingested respective section 694 and indicates that theanalysis is unfavorable when there is no match. When the analysis of theingested respective section is unfavorable, a second alternatedetermining approach step includes the determine storage module 688identifying the one or more of the plurality of data blocks 700 that arenot to be permanently stored.

With regards to the permanently storing the data, the store data module690, when the one or more of the plurality of data blocks is to bepermanently stored, stores the one or more of the plurality of datablocks 700 (e.g., in a permanent storage portion of memory 684) andgenerates a group of partial redundancy data based on the one or more ofthe plurality of data blocks 700 and in accordance with a dispersedstorage error coding function.

The store data module 690 generates the group of partial redundancy databased on the one or more of the plurality of data blocks 700 by a seriesof generating steps. A first generating step includes the store datamodule 690 generating a first partial redundancy data 696 for issuing toa first redundancy DS unit 674 based on at least some of the pluralityof data blocks 700 and a first encoding parameter of the dispersedstorage error coding function. A second generating step includes thestore data module 690 generating a second partial redundancy data 698for issuing to a second redundancy DS unit 676 based on at least anothersome of the plurality of data blocks 700 and a second encoding parameterof the dispersed storage error coding function. The generating the firstand second partial redundancy data 696 and 698 includes a variety ofredundancy data generating approaches. A first redundancy data approachincludes a series of steps. A first step includes arranging the one ormore of the plurality of data blocks 700 in a single row data matrix. Asecond generating step includes multiplying the single row data matrixby a first value of a first error encoding row of an encoding matrix toproduce the first partial redundancy data 696. A third step includesmultiplying the single row data matrix by a second value of the firsterror encoding row to produce the second partial redundancy data 698. Asecond redundancy data approach includes a series of alternative steps.A first alternative step includes exclusive ORing a first sub-set of theone or more of the plurality of data blocks 700 to produce the firstpartial redundancy data 696. A second alternative step includesexclusive ORing a second sub-set of the one or more of the plurality ofdata blocks 700 to produce the second partial redundancy data 698.

With regards to the permanently storing the other data, the store newdata module 692, when the one or more of the plurality of data blocks700 is not to be permanently stored performs a series of create new datasteps. In a first create new data step, the store new data module 692creates a new plurality of data blocks 704 from data blocks of theplurality of data blocks 700 that are to be permanently stored and datablocks from another data segment that are to be permanently stored. Thestore new data module 692 functions to create the new plurality of datablocks 704 including by identifying the other data segment as a datasegment for which partial redundancy data does not yet exist (e.g., anewly ingested data segment). The store new data module 692 furtherfunctions to permanently store the new plurality of data blocks 704(e.g., in the permanent storage portion of memory 684). In a secondcreate new data step, the store new data module 692 generates the groupof partial redundancy data based on the new plurality of data blocks 704in accordance with the dispersed storage error coding function (e.g.,for issuing as the first and second partial redundancy data 696 and 698to redundancy DS units 674 and 676).

FIG. 48C is a flowchart illustrating an example of improving storageefficiency. The method begins at step 710 where a processing module(e.g., of a dispersed storage (DS) unit of a set of DS units of adispersed storage network (DSN)) ingests a respective section of data ofa set of sections of the data. The set of DS units ingests the set ofsections. A large amount of data is divided into sets of partitions andeach of the sets of partitions is divided into a corresponding set ofsections. For the ingested respective section of data, the methodcontinues at step 712 where the processing module divides the ingestedrespective section of data into a plurality of data segments (e.g., inaccordance with a dispersed storage error coding function). For datasegments of the plurality of data segments, the method continues at step714 where the processing module divides the data segment into aplurality of data blocks (e.g., a row of a data matrix). The methodcontinues at step 716 where the processing module temporarily stores theplurality of data blocks.

The method continues at step 718 where the processing module determineswhether to not permanently store one or more of the plurality of datablocks. The processing module determines whether to not permanentlystore the one or more of the plurality of data blocks by a variety ofdetermining approaches. A determining approach includes the processingmodule performing a series of determining approach steps. A firstdetermining approach step includes determining that at least one otherDS unit (e.g., of ingesting DS units) of the set of DS units hasingested a respective section that is substantially similar to theingested respective section (e.g., by comparing sections and/orcomparing representations of the sections). Alternatively, the firstdetermining approach step includes determining that the DS unit hasingested the respective section that is substantially similar to theingested respective section (e.g., previously stored temporarily and/orpermanently by the DS unit).

In response to the determining that the at least one other DS unit hasingested the respective section that is substantially similar to theingested respective section, the processing module performs a seconddetermining approach step that includes a series of sub-steps. A firstsub-step includes determining whether a de-duplication function is to beapplied to the ingested respective section. When the de-duplicationfunction is to be applied, a second sub-step includes identifying theone or more of the plurality of data blocks that are not to bepermanently stored. When the de-duplication function is not to beapplied, the second sub-step indicating that the one or more of theplurality of data blocks are to be permanently stored.

Another determining approach, of the variety of determining approaches,includes the processing module performing a series of alternatedetermining approach steps. A first alternate determining approach stepincludes the processing module analyzing the ingested respective sectionin accordance with data analysis criteria. When the analysis of theingested respective section is unfavorable, a second alternatedetermining approach step includes the processing module identifying theone or more of the plurality of data blocks that are not to bepermanently stored.

The method branches to step 724 when the one or more of the plurality ofdata blocks is not to be permanently stored. The method continues tostep 720 when the one or more of the plurality of data blocks is to bepermanently stored. When the one or more of the plurality of data blocksis to be permanently stored, the method continues at step 720 where theprocessing module stores the one or more of the plurality of data blocks(e.g., in a permanent storage portion of a local memory).

The method continues at step 722 where the processing module generates agroup of partial redundancy data based on the one or more of theplurality of data blocks and in accordance with the dispersed storageerror coding function. The generating the group of partial redundancydata based on the one or more of the plurality of data blocks includesgenerating a first partial redundancy data for issuing to a firstredundancy DS unit based on at least some of the plurality of datablocks and a first encoding parameter of the dispersed storage errorcoding function and generating a second partial redundancy data forissuing to a second redundancy DS unit based on at least another some ofthe plurality of data blocks and a second encoding parameter of thedispersed storage error coding function. The generating the first andsecond partial redundancy data includes a variety of redundancy datagenerating approaches. A first redundancy data approach includes aseries of steps. A first step includes arranging the one or more of theplurality of data blocks in a single row data matrix. A secondgenerating step includes multiplying the single row data matrix by afirst value of a first error encoding row of an encoding matrix toproduce the first partial redundancy data. A third step includesmultiplying the single row data matrix by a second value of the firsterror encoding row to produce the second partial redundancy data. Asecond redundancy data approach includes an alternative series of steps.A first alternative step includes exclusive ORing a first sub-set of theone or more of the plurality of data blocks to produce the first partialredundancy data. A second alternative step includes exclusive ORing asecond sub-set of the one or more of the plurality of data blocks toproduce the second partial redundancy data.

When the one or more of the plurality of data blocks is not to bepermanently stored, the method continues at step 724 where theprocessing module creates a new plurality of data blocks from datablocks of the plurality of data blocks that are to be permanently storedand data blocks from another data segment that are to be permanentlystored. The creating the new plurality of data blocks includesidentifying the other data segment as a data segment for which partialredundancy data does not yet exist (e.g., from the cache memory the DSunit or from another DS unit). The method continues at step 726 wherethe processing module permanently stores the new plurality of datablocks (e.g., in the permanent storage portion of the local memory). Themethod continues at step 728 where the processing module generates thegroup of partial redundancy data based on the new plurality of datablocks in accordance with the dispersed storage error coding function.

FIG. 48D is a schematic block diagram of another example of a dispersedstorage network that includes a dispersed storage (DS) unit set 730. TheDS unit set 730 includes a DS unit 732, redundancy DS units 734-736, andingesting DS units 733. At least one of the ingesting DS units 733includes the DS unit 672. The DS unit set 730 includes at least a pillarwidth number of DS units including at least a decode threshold number ofingesting DS units 733 (e.g., including DS unit 732). The DS unit 732includes a computing device 738. The computing device 738 may beimplemented utilizing at least one of a server, a storage unit, astorage server, a storage module, a dispersed storage (DS) processingunit, a DS unit, a distributed storage and task (DST) execution unit, auser device, a DST processing unit, and a DST processing module. Thecomputing device 738 includes a DS module 740 and a memory 742. Thememory 742 may be implemented utilizing one or more of a memory device,a memory module, an optical memory, a magnetic memory, a solid-statememory, a temporary storage module, a permanent storage module, and astorage server. The DS module 740 may be implemented utilizing at leastone of a processing module, one or more central processing unit cores, asoftware algorithm, a DST client module, and a DS processing module. TheDS module 740 includes an identify deletion module 744, a new datamodule 746, and a new redundancy data module 748.

The set of DS units 730 stores a large amount of data and may ingest aportion of the large amount of data. When ingesting, the set of DS units730 divides the large amount of data into a set of partitions anddivides each of the sets of partitions into a set of sections. The DSunit 732 functions include identifying data for deletion, permanentlystoring new data, and updating redundancy data.

With regards to identifying data for deletion, the identify deletionmodule 744 determines that one or more data blocks of a permanentlystored plurality of data blocks 750 are to be deleted. The identifydeletion module 744 identifies that the one or more data blocks of thepermanently stored plurality of data blocks 750 are to be deleted by avariety of identification approaches. An identification approach of thevariety of identification approaches includes a series of identificationsteps. A first identification step includes the identify deletion module744 determining that at least one other DS unit (e.g., ingesting DS unit733) of the set of DS units 730 is storing data blocks that aresubstantially similar to the one or more data blocks 750. For example,the identify deletion module 744 receives data block storage information752 from at least one other ingesting DS unit 733 where the data blockstorage information 752 includes the at least one of the data blocksthat are substantially similar to the one or more data blocks 750 and arepresentation (e.g., a result of a deterministic function applied tothe one or more data blocks that are substantially similar) of the atleast one of the data blocks that are substantially similar to the oneor more data blocks 750. The example continues where the identifydeletion module 744 determines that the at least one other DS unit isstoring data blocks that are substantially similar to the one or moredata blocks 750 when a comparison of the representation of the at leastone of the data blocks that are substantially similar compares favorably(e.g., substantially the same) to a representation of the one or moredata blocks 750.

A second identification step of the identification approach includes theidentify deletion module 744, in response to the determining that the atleast one other DS unit is storing data blocks that are substantiallysimilar to the one or more data blocks 750, performs a series ofsub-steps. A first sub-step includes the identify deletion module 744determining whether a de-duplication function is to be applied to theone or more data blocks 750. The determining may be based on one or moreof how many data blocks stored by the other DS units are substantiallysimilar to the one or more data blocks 750, a desired maximum number ofsubstantially similar data blocks, identities of the other DS units,task execution performance availability levels of the other DS units,storage capacity availability levels of the other DS units, a data typeindicator, a lookup, a query, a replication goal, a storage goal,identity of the DS unit 732, and a performance goal. When thede-duplication function is to be applied, a second sub-step includes theidentify deletion module 744 determining that the one or more datablocks 750 are to be deleted. When the de-duplication function is not tobe applied, the second sub-step includes the identify deletion module744 determining that the one or more data blocks 750 are not to bedeleted.

Another identification approach of the variety of identificationapproaches includes a series of alternative identification steps. Afirst alternative identification step includes the identify deletionmodule 744 analyzing the permanently stored plurality of data blocks 750in accordance with data analysis criteria. For example, the identifydeletion module 744 compares the permanently stored plurality of datablocks 752 to a data file identifier of the data analysis criteria,determines that the permanently stored plurality of data blocks 750 isassociated with the data file identifier, and indicates that theanalysis is unfavorable. When the analysis of the permanently storedplurality of data blocks 750 is unfavorable, a second alternativeidentification step includes the identify deletion module 744determining that the one or more data blocks 750 of the permanentlystored plurality of data blocks are to be deleted.

With regards to permanently storing new data, the new data module 746,in response to the determining that the one or more data blocks 750 areto be deleted, performs a series of storing steps. In a first storingstep, the new data module 746 obtains a group of partial redundancy data754 for the permanently stored plurality of data blocks 750. Theobtaining includes at least one of retrieving from memory 742,retrieving from the redundancy DS units 734-736, and generating based onthe permanently stored plurality of data blocks 750 and in accordancewith a dispersed storage error coding function. In a second storingstep, the new data module 746 identifies a temporarily stored pluralityof data blocks 756 for which partial redundancy data does not yet exist(e.g., based on at least one of a query, a lookup, and retrieving thetemporarily stored plurality of data blocks 756 from memory 742). In athird storing step, the new data module 746 creates a new plurality ofdata blocks 758 from data blocks of the permanently stored plurality ofdata blocks 750 that are to remain permanently stored and data blocksfrom the temporarily stored plurality of data blocks 756 that are to bepermanently stored (e.g., based on data block for deletionidentification). In a fourth storing step, the new data module 746permanently stores the new plurality of data blocks 758 (e.g., in thememory 742).

With regards to updating redundancy data, the new redundancy data module748 performs a series of updating steps. In a first updating step, thenew redundancy data module 748 generates a new group of partialredundancy data 760 based on the new plurality of data blocks 758 and inaccordance with the dispersed storage error coding function. The newredundancy data module 748 generates the new group of partial redundancydata 760 based on the new plurality of data blocks 758 by a series ofgenerating steps. A first generating step includes the new redundancydata module 748 generating a first partial redundancy data for issuingto a first redundancy DS unit 734 as part of the new group of partialredundancy data 760 based on at least some of the new plurality of datablocks 758 and a first encoding parameter of the dispersed storage errorcoding function. A second generating step includes the new redundancydata module 748 generating a second partial redundancy data for issuingto a second redundancy DS unit 736 as part of the new group of partialredundancy data 760 based on at least another some of the new pluralityof data blocks 758 and a second encoding parameter of the dispersedstorage error coding function.

The generating the first and second partial redundancy data may furtherinclude a variety of redundancy data generating approaches. A firstredundancy data approach includes a series of steps. A first stepincludes arranging the new plurality of data blocks 758 in a single rowdata matrix. A second generating step includes multiplying the singlerow data matrix by a first value of a first error encoding row of anencoding matrix to produce the first partial redundancy data. A thirdstep includes multiplying the single row data matrix by a second valueof the first error encoding row to produce the second partial redundancydata. A second redundancy data approach includes an alternative seriesof steps. A first alternative step includes exclusive ORing a firstsub-set of the one or more of the new plurality of data blocks 758 toproduce the first partial redundancy data. A second alternative stepincludes exclusive ORing a second sub-set of the new plurality of datablocks 758 to produce the second partial redundancy data.

In a second updating step of updating the redundancy data, the newredundancy data module 748 sends the new group of partial redundancydata 760 and the group of partial redundancy data 754 such that theredundancy data DS units 734-736 are able to generate redundancy dataregarding the new plurality of data blocks 758. Alternatively, the newredundancy data module 748 performs an exclusiveOR function on the newgroup of partial redundancy data 760 and the group of partial redundancydata 754 to produce an alternative of the new redundancy data 760.

The redundancy DS units 734-736 generate the redundancy data byperforming the exclusiveOR function on the group of partial redundancydata 754 and a previous redundancy data (e.g., stored by a correspondingredundancy DS unit) to produce an updated redundancy data that excludesredundancy data associated with the one or more data blocks 750. Next,the redundancy DS units 734-736 performs the exclusiveOR function on theupdated redundancy data and the new group of partial redundancy data 760to produce the redundancy data (e.g., now associated with the newplurality of data blocks 758).

FIG. 48E is a flowchart illustrating another example of improvingstorage efficiency. The method begins at step 762 where a processingmodule (e.g., of a dispersed storage (DS) unit of a set of DS units of adispersed storage network (DSN)) determining that one or more datablocks of a permanently stored plurality of data blocks are to bedeleted. The set of DS units ingests a large amount of data, where thelarge amount of data is divided into sets of partitions and each of thesets of partitions is divided into a set of sections. For each ingestedrespective section of data, a corresponding ingesting DS unit of the setof DS units divides the ingested respective section of data into aplurality of data segments (e.g., in accordance with a dispersed storageerror coding function). For data segments of the plurality of datasegments, the corresponding ingesting DS unit divides the data segmentinto a plurality of data blocks (e.g., a row of a data matrix) andstores the plurality of data blocks as the permanently stored pluralityof data blocks.

The processing module determines that the one or more data blocks of thepermanently stored plurality of data blocks are to be deleted by avariety of identification approaches. An identification approach of thevariety of identification approaches includes a series of identificationsteps. A first identification step includes the processing moduledetermining that at least one other DS unit (e.g., an ingesting DS unit)of the set of DS units is storing data blocks that are substantiallysimilar to the one or more data blocks. For example, the processingmodule receives data block storage information from at least one otheringesting DS unit where the data block storage information includes theat least one of the data blocks that are substantially similar to theone or more data blocks and a representation (e.g., a result of adeterministic function applied to the one or more data blocks that aresubstantially similar) of the at least one of the data blocks that aresubstantially similar to the one or more data blocks. The examplecontinues where the processing module determines that the at least oneother DS unit is storing data blocks that are substantially similar tothe one or more data blocks when a comparison of the representation ofthe at least one of the data blocks that are substantially similarcompares favorably (e.g., substantially the same) to a representation ofthe one or more data blocks.

A second identification step of the identification approach includes theprocessing module, in response to the determining that the at least oneother DS unit is storing data blocks that are substantially similar tothe one or more data blocks, performs a series of sub-steps. A firstsub-step includes determining whether a de-duplication function is to beapplied to the one or more data blocks 750. The determining may be basedon one or more of how many data blocks stored by the other DS units aresubstantially similar to the one or more data blocks, a desired maximumnumber of substantially similar data blocks, identities of the other DSunits, task execution performance availability levels of the other DSunits, storage capacity availability levels of the other DS units, adata type indicator, a lookup, a query, a replication goal, a storagegoal, identity of the DS unit, and a performance goal. When thede-duplication function is to be applied, a second sub-step includesdetermining that the one or more data blocks are to be deleted. When thede-duplication function is not to be applied, the second sub-stepincludes determining that the one or more data blocks are not to bedeleted.

Another identification approach of the variety of identificationapproaches includes a series of alternative identification steps. Afirst alternative identification step includes the processing moduleanalyzing the permanently stored plurality of data blocks in accordancewith data analysis criteria. For example, the processing module comparesthe permanently stored plurality of data blocks to a pattern of the dataanalysis criteria, determines that the permanently stored plurality ofdata blocks includes the pattern, and indicates that the analysis isunfavorable. When the analysis of the permanently stored plurality ofdata blocks is unfavorable, a second alternative identification stepincludes the processing module determining that the one or more datablocks of the permanently stored plurality of data blocks are to bedeleted.

In response to the determining that the one or more data blocks are tobe deleted, the method continues at step 764 where the processing moduleobtains a group of partial redundancy data for the permanently storedplurality of data blocks (e.g., generate, retrieve, receive). The methodcontinues at step 766 where the processing module identifies atemporarily stored plurality of data blocks for which partial redundancydata does not yet exist (e.g., based on at least one of a query, a test,and receiving a message). The method continues at step 768 where theprocessing module creates a new plurality of data blocks from datablocks of the permanently stored plurality of data blocks that are toremain permanently stored and data blocks from the temporarily storedplurality of data blocks that are to be permanently stored (e.g.,identifying data blocks that are not to be deleted from a previousidentification of data blocks for deletion). The method continues atstep 770 where the processing module permanently stores the newplurality of data blocks (e.g., stores in a permanent storage portion ofa local memory).

The method continues at step 772 where the processing module generates anew group of partial redundancy data based on the new plurality of datablocks and in accordance with the dispersed storage error codingfunction. The processing module generates the new group of partialredundancy data based on the new plurality of data blocks by a series ofgenerating steps. A first generating step includes the processing modulegenerating a first partial redundancy data for issuing to a firstredundancy DS unit as part of the new group of partial redundancy databased on at least some of the new plurality of data blocks and a firstencoding parameter of the dispersed storage error coding function. Asecond generating step includes processing module generating a secondpartial redundancy data for issuing to a second redundancy DS unit aspart of the new group of partial redundancy data based on at leastanother some of the new plurality of data blocks and a second encodingparameter of the dispersed storage error coding function.

The generating the first and second partial redundancy data may furtherinclude a variety of redundancy data generating approaches. A firstredundancy data approach includes a series of steps. A first stepincludes arranging the new plurality of data blocks in a single row datamatrix. A second generating step includes multiplying the single rowdata matrix by a first value of a first error encoding row of anencoding matrix to produce the first partial redundancy data. A thirdstep includes multiplying the single row data matrix by a second valueof the first error encoding row to produce the second partial redundancydata. A second redundancy data approach includes an alternative seriesof steps. A first alternative step includes exclusive ORing a firstsub-set of the one or more of the new plurality of data blocks toproduce the first partial redundancy data. A second alternative stepincludes exclusive ORing a second sub-set of the new plurality of datablocks to produce the second partial redundancy data.

The method continues at step 774 where the processing module sends thenew group of partial redundancy data and the group of partial redundancydata such that redundancy data DS units are able to generate redundancydata regarding the new plurality of data blocks. Alternatively, theprocessing module an exclusiveOR function on the new group of partialredundancy data and the group of partial redundancy data to produce analternative of the new redundancy data for issuing to the redundancydata DS units.

FIG. 49 is a flowchart illustrating an example of encrypting data, whichincludes similar steps to FIG. 5. The method begins with step 126 ofFIG. 5 where a processing module (e.g., of a distributed storage andtask (DST) client module) receives data and a corresponding task. Themethod continues at step 780 where the processing module selects one ormore DST execution units from a plurality of DST execution units forexecution of the task based on a decryption capability level associatedwith each of the one or more DST execution units. The selecting includesdetermining a desired number of DST execution units, obtainingdecryption capability levels associated with at least some of theplurality of DST execution units, and selecting the desired number ofDST execution units wherein each selected DST execution unit isassociated with a corresponding encryption capability level thatcompares favorably with a desired decryption capability level. Thedecryption capability level includes one or more indicators forencryption algorithm support, key support, and availability. Theprocessing module selects a DST execution unit when an associateddecryption capability level includes required decryption capabilities(e.g., based on a lookup, based on the data, based on a receivedsecurity requirement).

The method continues with steps 130-136 of FIG. 5 where the processingmodule determines processing parameters of the data based on the numberof DST execution units, determines task partitioning based on the DSTexecution units in the processing parameters, processes the data inaccordance with the processing parameters to produce slice groupings,and partitions the task based on the task partitioning to producepartial tasks. The method continues at step 782 where the processingmodule obtains a random key. The obtaining may be based on one or moreof a random number generator, querying a random key generator, receivingthe random key, and a lookup. The method continues at step 784 where theprocessing module facilitates storing the random key in a distributedstorage and task network (DSTN). For example, the processing moduledispersed storage error encodes the random key utilizing an all ornothing transformation followed by application of an informationdispersal algorithm to produce a set of encoded key slices and sends theset of encoded key slices to the DSTN for storage therein. As anotherexample, the processing module generates a store securely task requestand sends the store securely task request and the random key to a DSTclient module to store the random key in the DSTN.

The method continues at step 786 where the processing module encryptsone or more slices of each slice groupings of the one more slicegroupings utilizing the random key to produce encrypted slice groupings.For example, the processing module encrypts a first slice of slicegrouping 1 to produce a first encrypted slice of encrypted slicegrouping 1, encrypts a second slice of slice grouping 1 to produce asecond encrypted slice of encrypted slice grouping 1, encrypts a thirdslice of slice grouping 1 to produce a third encrypted slice ofencrypted slice grouping 1, etc. The method continues at step 788 wherethe processing module sends the encrypted slice groupings andcorresponding partial tasks to the DST execution units.

FIG. 50 is a flowchart illustrating an example of decrypting data. Themethod begins at step 790 where a processing module (e.g., of adistributed storage and task (DST) execution unit) receives at least onepartial task and an encrypted slice grouping (e.g., from a DST clientmodule). The method continues at step 792 where the processing modulestores the encrypted slice grouping in a local memory. The methodcontinues at step 794 where the processing module facilitates retrievingan associated random key from a distributed storage and task network(DSTN). The retrieving includes identifying at least a decode thresholdnumber of DST execution units associated with storing random key,sending a retrieval request to the decode threshold number of DSTexecution units, receiving at least a decode threshold number ofretrieval responses, and decoding the at least the decode thresholdnumber of retrieval responses to reproduce the associated random key.The identifying may be based on one or more of interpreting an embeddedassociated random key identifier (e.g., a DSTN address source name) fromat least one of the one partial task and the encrypted slice groupingand receiving a storage location of the associated random key.

The method continues at step 796 where the processing module retrievesthe encrypted slice grouping from the local memory. The method continuesat step 798 where the processing module decrypts one or more slices ofthe encrypted slice grouping utilizing the random key to produce a slicegrouping. For example, the processing module decrypts a first encryptedslice of encrypted slice grouping 1 to produce a first slice of slicegrouping 1, decrypts a second encrypted slice of encrypted slicegrouping 1 to produce a second slice of slice grouping 1, etc. Themethod continues at step 800 where the processing module facilitatesexecuting at least some of the at least one partial task on the slicegrouping to produce at least one partial result.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a first distributedstorage and task (DST) execution unit of a distributed computing system,the method comprises: determining that partial task processing resourcesof the first DST execution unit are projected to be available based on afirst local task queue, a first expected partial task performancefactor, and a first expected partial task allocation factor, wherein thefirst DST execution unit is one of a set of DST execution units, whereinthe set of DST execution units is assigned to perform tasks on largeamounts of data, wherein each of the large amounts of data ispartitioned into data partitions, wherein each of the data partitions isfurther divided into data groups, wherein each of the tasks is dividedinto a set of partial tasks, and wherein various DST execution units ofthe set of DST execution units are assigned various partial tasks ofvarious ones of the sets of partial tasks to perform on various ones ofthe data groups of various ones of the data partitions of various onesof the large amounts of data; ascertaining that partial task processingresources of a second DST execution unit of the set of DST executionunits are projected to be overburdened based on a second local taskqueue, a second expected partial task performance factor, and a secondexpected partial task allocation factor; receiving, from the second DSTexecution unit, a partial task assigned to the second DST execution unitin accordance with a partial task allocation transfer policy to producean allocated partial task; and executing the allocated partial task. 2.The method of claim 1, wherein the determining that the partial taskprocessing resources of the first DST execution unit are projected to beavailable comprises: determining that a current snapshot of the firstlocal task queue compares favorably to a current queue threshold;determining that a projected snapshot of the first local task queuecompares favorably to a projected queue threshold that is based on atleast one of the first expected partial task performance factor and thefirst expected partial task allocation factor; and when the currentsnapshot of the first local task queue compares favorably to the currentqueue threshold and the projected snapshot of the first local task queuecompares favorably to the projected queue threshold, indicating that thepartial task processing resources of the first DST execution unit areprojected to be available.
 3. The method of claim 1, wherein theascertaining that the partial task processing resources of the secondDST execution unit of the set of DST execution units are projected to beoverburdened comprises: ascertaining that a current snapshot of thesecond local task queue compares unfavorably to a current queuethreshold; ascertaining that a projected snapshot of the second localtask queue compares unfavorably to a projected queue threshold that isbased on at least one of the second expected partial task performancefactor and the second expected partial task allocation factor; and whenat least one of the current snapshot of the second local task queuecompares unfavorably to the current queue threshold and the projectedsnapshot of the second local task queue compares unfavorably to theprojected queue threshold, indicating that the partial task processingresources of the second DST execution unit are projected to beoverburdened.
 4. The method of claim 1, wherein the receiving thepartial task comprises: determining unexecuted partial tasks assigned tothe second DST execution unit; selecting one of the unexecuted partialtasks based on execution capabilities of the first DST execution unitand the second expected partial task performance factor; and receiving,from the second DST execution unit, the selected partial task and acorresponding data group.
 5. The method of claim 1 further comprises:identifying the second DST execution unit based on a common taskcriteria with the first DST execution unit, wherein the common taskcriteria includes one or more of a common site, a common large amount ofdata, a common task allocation unit, and a common data partition.
 6. Themethod of claim 1, wherein the executing the allocated partial taskcomprises: updating the first local task queue to include the allocatedpartial task; updating the first expected partial task performancefactor based on the allocated partial task, and updating the firstexpected partial task allocation factor based on the allocated partialtask.
 7. The method of claim 1 further comprises: updating the secondlocal task queue by removing the partial task; updating the secondexpected partial task performance factor based on removing the partialtask; and updating the second expected partial task allocation factorbased on removing the partial task.
 8. A method for execution by a firstdistributed storage and task (DST) execution unit of a distributedcomputing system, the method comprises: receiving assignment ofexecuting first partial tasks on first contiguous data slice groups of afirst partition of a plurality of data partitions, wherein the first DSTexecution unit is one of a set of DST execution units, wherein a largeamount of data is divided into the plurality of data partitions, whereineach data partition is dispersed storage error encoded to produce a setof data slice groups, wherein a first sub-set of the set of data slicegroups includes contiguous data slice groups and a second sub-set of theset of data slice groups includes error coded data slice groups;determining a first expected partial task performance factor based on acomparison of the first partition of the plurality of data partitions tothe plurality of data partitions; determining that partial taskprocessing resources of the first DST execution unit are projected to beavailable based on the assignment of the first contiguous data slicegroups, the first expected partial task performance factor, and a firstexpected partial task allocation factor; ascertaining that partial taskprocessing resources of a second DST execution unit of the set of DSTexecution units are projected to be overburdened based on assignment ofsecond contiguous data slice groups assigned to the second DST unit, asecond expected partial task performance factor, and a second expectedpartial task allocation factor; receiving, from the second DST executionunit, a partial task and a corresponding one of the second contiguousdata slice groups in accordance with a partial task allocation transferpolicy; and executing the partial task on the corresponding one of thesecond contiguous data slice groups.
 9. The method of claim 8, whereinthe receiving the partial task comprises: determining that the partialtask regarding the corresponding one of the second contiguous data slicegroups is pending execution by the second DST execution unit; andrequesting the partial task and the corresponding one of the secondcontiguous data slice groups.
 10. The method of claim 8 furthercomprises: determining the second expected partial task performancefactor based on a comparison of a second partition of the plurality ofdata partitions to the plurality of data partitions, wherein the secondDST execution unit is assigned to execute second partial tasks on secondcontiguous data slice groups of the second partition of the plurality ofdata partitions.
 11. A dispersed storage (DS) module of firstdistributed storage and task (DST) execution unit of a distributedcomputing system comprises: a first module, when operable within acomputing device, causes the computing device to: determine that partialtask processing resources of the first DST execution unit are projectedto be available based on a first local task queue, a first expectedpartial task performance factor, and a first expected partial taskallocation factor, wherein the first DST execution unit is one of a setof DST execution units, wherein the set of DST execution units isassigned to perform tasks on large amounts of data, wherein each of thelarge amounts of data is partitioned into data partitions, wherein eachof the data partitions is further divided into data groups, wherein eachof the tasks is divided into a set of partial tasks, and wherein variousDST execution units of the set of DST execution units are assignedvarious partial tasks of various ones of the sets of partial tasks toperform on various ones of the data groups of various ones of the datapartitions of various ones of the large amounts of data; a secondmodule, when operable within the computing device, causes the computingdevice to: ascertain that partial task processing resources of a secondDST execution unit of the set of DST execution units are projected to beoverburdened based on a second local task queue, a second expectedpartial task performance factor, and a second expected partial taskallocation factor; a third module, when operable within the computingdevice, causes the computing device to: receive, from the second DSTexecution unit, a partial task assigned to the second DST execution unitin accordance with a partial task allocation transfer policy to producean allocated partial task; and a fourth module, when operable within thecomputing device, causes the computing device to: execute the allocatedpartial task.
 12. The DS module of claim 11, wherein the first modulefunctions to determine that the partial task processing resources of thefirst DST execution unit are projected to be available by: determiningthat a current snapshot of the first local task queue compares favorablyto a current queue threshold; determining that a projected snapshot ofthe first local task queue compares favorably to a projected queuethreshold that is based on at least one of the first expected partialtask performance factor and the first expected partial task allocationfactor; and when the current snapshot of the first local task queuecompares favorably to the current queue threshold and the projectedsnapshot of the first local task queue compares favorably to theprojected queue threshold, indicating that the partial task processingresources of the first DST execution unit are projected to be available.13. The DS module of claim 11, wherein the second module functions toascertain that the partial task processing resources of the second DSTexecution unit of the set of DST execution units are projected to beoverburdened by: ascertaining that a current snapshot of the secondlocal task queue compares unfavorably to a current queue threshold;ascertaining that a projected snapshot of the second local task queuecompares unfavorably to a projected queue threshold that is based on atleast one of the second expected partial task performance factor and thesecond expected partial task allocation factor; and when at least one ofthe current snapshot of the second local task queue compares unfavorablyto the current queue threshold and the projected snapshot of the secondlocal task queue compares unfavorably to the projected queue threshold,indicating that the partial task processing resources of the second DSTexecution unit are projected to be overburdened.
 14. The DS module ofclaim 11, wherein the third module functions to receive the partial taskby: determining unexecuted partial tasks assigned to the second DSTexecution unit; selecting one of the unexecuted partial tasks based onexecution capabilities of the first DST execution unit and the secondexpected partial task performance factor; and receiving, from the secondDST execution unit, the selected partial task and a corresponding datagroup.
 15. The DS module of claim 11 further comprises: the secondmodule further functions to identify the second DST execution unit basedon a common task criteria with the first DST execution unit, wherein thecommon task criteria includes one or more of a common site, a commonlarge amount of data, a common task allocation unit, and a common datapartition.
 16. The DS module of claim 11, wherein the fourth modulefunctions to execute the partial task by: updating the first local taskqueue to include the allocated partial task; updating the first expectedpartial task performance factor based on the allocated partial task, andupdating the first expected partial task allocation factor based on theallocated partial task.
 17. The DS module of claim 11 further comprises:the third module further functions to: update the second local taskqueue by removing the partial task; update the second expected partialtask performance factor based on removing the partial task; and updatethe second expected partial task allocation factor based on removing thepartial task.
 18. A dispersed storage (DS) module of a first distributedstorage and task (DST) execution unit of a distributed computing systemcomprises: a first module, when operable within a computing device,causes the computing device to: receive assignment of executing firstpartial tasks on first contiguous data slice groups of a first partitionof a plurality of data partitions, wherein the first DST execution unitis one of a set of DST execution units, wherein a large amount of datais divided into the plurality of data partitions, wherein each datapartition is dispersed storage error encoded to produce a set of dataslice groups, wherein a first sub-set of the set of data slice groupsincludes contiguous data slice groups and a second sub-set of the set ofdata slice groups includes error coded data slice groups; a secondmodule, when operable within the computing device, causes the computingdevice to: determine a first expected partial task performance factorbased on a comparison of the first partition of the plurality of datapartitions to the plurality of data partitions; and determine thatpartial task processing resources of the first DST execution unit areprojected to be available based on the assignment of the firstcontiguous data slice groups, the first expected partial taskperformance factor, and a first expected partial task allocation factor;a third module, when operable within the computing device, causes thecomputing device to: ascertain that partial task processing resources ofa second DST execution unit of the set of DST execution units areprojected to be overburdened based on assignment of second contiguousdata slice groups assigned to the second DST unit, a second expectedpartial task performance factor, and a second expected partial taskallocation factor; and a fourth module, when operable within thecomputing device, causes the computing device to: receive, from thesecond DST execution unit, a partial task and a corresponding one of thesecond contiguous data slice groups in accordance with a partial taskallocation transfer policy; and execute the partial task on thecorresponding one of the second contiguous data slice groups.
 19. The DSmodule of claim 18, wherein the fourth module functions to receive thepartial task by: determining that the partial task regarding thecorresponding one of the second contiguous data slice groups is pendingexecution by the second DST execution unit; and requesting the partialtask and the corresponding one of the second contiguous data slicegroups.
 20. The DS module of claim 18 further comprises: the thirdmodule further functions to determine the second expected partial taskperformance factor based on a comparison of a second partition of theplurality of data partitions to the plurality of data partitions,wherein the second DST execution unit is assigned to execute secondpartial tasks on second contiguous data slice groups of the secondpartition of the plurality of data partitions.